Add plots, tex files, and missing MOF structure (#60)

.gitattributes

@@ -8,3 +8,5 @@ examples/mof/classification/MACE-MPA.pkl filter=lfs diff=lfs merge=lfs -text
 examples/mof/classification/MACE-MP(M).pkl filter=lfs diff=lfs merge=lfs -text
 examples/mof/classification/MatterSim.pkl filter=lfs diff=lfs merge=lfs -text
 examples/mof/classification/ORBv2.pkl filter=lfs diff=lfs merge=lfs -text
+examples/c2db/*.db filter=lfs diff=lfs merge=lfs -text
+examples/c2db/*.parquet filter=lfs diff=lfs merge=lfs -text

.github/workflows/test.yaml

@@ -21,13 +21,11 @@ jobs:
     - name: Checkout PR with full history
       uses: actions/checkout@v4
       with:
-        lfs: true
         fetch-depth: 0
 
     - name: Install uv
       uses: astral-sh/setup-uv@v6
       with:
-        version: "latest"
         enable-cache: true
         cache-dependency-glob: "pyproject.toml"
 
@@ -51,14 +49,14 @@ jobs:
       env:
         PREFECT_API_KEY: ${{ secrets.PREFECT_API_KEY }}
         PREFECT_API_URL: ${{ secrets.PREFECT_API_URL }}
-      run: pytest -vra -n 5 --dist=loadscope tests
+      run: pytest -vra -n 5 --basetemp=./.pytest_temp tests
 
     - name: Squash commits and trial push to Hugging Face
       if: github.event_name == 'pull_request'
       id: trial_push
       env:
         HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        TRIAL_BRANCH: trial-sync-${{ github.sha }}
+        TRIAL_BRANCH: trial-sync-${{ github.sha }}-${{ matrix.python-version }}
       run: |
         # Configure Git user identity
         git config user.name "github-actions[ci]"
@@ -69,9 +67,15 @@ jobs:
         git reset --soft $BASE
         git commit -m "Squashed commit from PR #${{ github.event.pull_request.number }}"
 
+        # Install Git LFS
+        sudo apt-get update
+        sudo apt-get install -y git-lfs
+        git lfs install
+
         # Setup LFS
         git lfs fetch
         git lfs checkout
+        # git config lfs.allowincompletepush true
 
         # Push to temporary branch on Hugging Face
         git push -f https://HF_USERNAME:[email protected]/spaces/atomind/mlip-arena HEAD:refs/heads/$TRIAL_BRANCH
@@ -80,6 +84,6 @@ jobs:
       if: steps.trial_push.outcome == 'success'
       env:
         HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        TRIAL_BRANCH: trial-sync-${{ github.sha }}
+        TRIAL_BRANCH: trial-sync-${{ github.sha }}-${{ matrix.python-version }}
       run: |
         git push https://HF_USERNAME:[email protected]/spaces/atomind/mlip-arena --delete $TRIAL_BRANCH || true

examples/c2db/ALIGNN.parquet

@@ -0,0 +1,3 @@
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+oid sha256:ce4d250afce0a7ef62dd27c5531b1e3a91f761035cc595e64ff6aae225e4ad73
+size 272171

examples/c2db/CHGNet.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6063fa72efb16a5255b79f5e1a03bd13409ed129016496ff1f494c6f83b98be
+size 292909

examples/c2db/M3GNet.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32e1517a85a1b64f12fb262a0948a95be58c69edde133ce7ddf683154b8f2a95
+size 290358

examples/c2db/MACE-MP(M).parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f722eac6799bfecaa02188d59475862895a639cc596fa8b7d1e9d2b96cfb415b
+size 293633

examples/c2db/MACE-MPA.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3ea679b5f6c9940358a2121a496544be91ba01ed8383509c65773f9fc69b9ec
+size 293820

examples/c2db/MatterSim.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d150c1b31b99ddcbbf21401189289aead13791c683aa379d75163b8bc4dbc6b4
+size 293177

examples/c2db/ORBv2.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2496f96d4aff1536936e58e65c1d608cc1953d41006221ba62ea2daab23f30b
+size 293012

examples/c2db/SevenNet.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c2ee18ce70f24f70e65d70c2e54151e86dd0ccb3e412b8fbbc572e44e8bf5e8
+size 293973

examples/c2db/analysis.ipynb

@@ -0,0 +1,408 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0625f0a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "from ase.db import connect\n",
+    "\n",
+    "random.seed(0)\n",
+    "\n",
+    "DATA_DIR = Path(\".\")\n",
+    "\n",
+    "db = connect(DATA_DIR / \"c2db.db\")\n",
+    "random_indices = random.sample(range(1, len(db) + 1), 1000)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "005708b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "\n",
+    "import pandas as pd\n",
+    "import phonopy\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "from mlip_arena.models import MLIPEnum\n",
+    "\n",
+    "for row, model in tqdm(\n",
+    "    itertools.product(db.select(filter=lambda r: r[\"id\"] in random_indices), MLIPEnum)\n",
+    "):\n",
+    "    uid = row[\"uid\"]\n",
+    "\n",
+    "    if Path(f\"{model.name}.parquet\").exists():\n",
+    "        df = pd.read_parquet(f\"{model.name}.parquet\")\n",
+    "        if uid in df[\"uid\"].unique():\n",
+    "            continue\n",
+    "    else:\n",
+    "        df = pd.DataFrame(columns=[\"model\", \"uid\", \"eigenvalues\", \"frequencies\"])\n",
+    "\n",
+    "    try:\n",
+    "        path = Path(model.name) / uid\n",
+    "        phonon = phonopy.load(path / \"phonopy.yaml\")\n",
+    "        frequencies = phonon.get_frequencies(q=(0, 0, 0))\n",
+    "\n",
+    "        data = np.load(path / \"elastic.npz\")\n",
+    "\n",
+    "        eigenvalues = data[\"eigenvalues\"]\n",
+    "\n",
+    "        new_row = pd.DataFrame(\n",
+    "            [\n",
+    "                {\n",
+    "                    \"model\": model.name,\n",
+    "                    \"uid\": uid,\n",
+    "                    \"eigenvalues\": eigenvalues,\n",
+    "                    \"frequencies\": frequencies,\n",
+    "                }\n",
+    "            ]\n",
+    "        )\n",
+    "\n",
+    "        df = pd.concat([df, new_row], ignore_index=True)\n",
+    "        df.drop_duplicates(subset=[\"model\", \"uid\"], keep=\"last\", inplace=True)\n",
+    "\n",
+    "        df.to_parquet(f\"{model.name}.parquet\", index=False)\n",
+    "    except Exception:\n",
+    "        pass\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b8d87638",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uids = []\n",
+    "stabilities = []\n",
+    "for row in db.select(filter=lambda r: r[\"id\"] in random_indices):\n",
+    "    stable = row.key_value_pairs[\"dyn_stab\"]\n",
+    "    if stable.lower() == \"unknown\":\n",
+    "        stable = None\n",
+    "    else:\n",
+    "        stable = True if stable.lower() == \"yes\" else False\n",
+    "    uids.append(row.key_value_pairs[\"uid\"])\n",
+    "    stabilities.append(stable)\n",
+    "\n",
+    "\n",
+    "stabilities = np.array(stabilities)\n",
+    "\n",
+    "(stabilities == True).sum(), (stabilities == False).sum(), (stabilities == None).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3c516a7",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "0052d0ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt\n",
+    "from sklearn.metrics import (\n",
+    "    ConfusionMatrixDisplay,\n",
+    "    classification_report,\n",
+    "    confusion_matrix,\n",
+    ")\n",
+    "\n",
+    "from mlip_arena.models import MLIPEnum\n",
+    "\n",
+    "thres = -1e-7\n",
+    "\n",
+    "select_models = [\n",
+    "    \"ALIGNN\",\n",
+    "    \"CHGNet\",\n",
+    "    \"M3GNet\",\n",
+    "    \"MACE-MP(M)\",\n",
+    "    \"MACE-MPA\",\n",
+    "    \"MatterSim\",\n",
+    "    \"ORBv2\",\n",
+    "    \"SevenNet\",\n",
+    "]\n",
+    "\n",
+    "with plt.style.context(\"default\"):\n",
+    "    # plt.rcParams.update({\n",
+    "    #     # \"title.fontsize\": 10,\n",
+    "    #     \"axes.titlesize\": 10,\n",
+    "    #     \"axes.labelsize\": 8,\n",
+    "    # })\n",
+    "\n",
+    "    SMALL_SIZE = 8\n",
+    "    MEDIUM_SIZE = 10\n",
+    "    BIGGER_SIZE = 12\n",
+    "    plt.rcParams.update(\n",
+    "        {\n",
+    "            \"font.size\": SMALL_SIZE,\n",
+    "            \"axes.titlesize\": MEDIUM_SIZE,\n",
+    "            \"axes.labelsize\": MEDIUM_SIZE,\n",
+    "            \"xtick.labelsize\": MEDIUM_SIZE,\n",
+    "            \"ytick.labelsize\": MEDIUM_SIZE,\n",
+    "            \"legend.fontsize\": SMALL_SIZE,\n",
+    "            \"figure.titlesize\": BIGGER_SIZE,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "    fig, axs = plt.subplots(\n",
+    "        nrows=int(np.ceil(len(MLIPEnum) / 4)),\n",
+    "        ncols=4,\n",
+    "        figsize=(6, 3 * int(np.ceil(len(select_models) / 4))),\n",
+    "        sharey=True,\n",
+    "        sharex=True,\n",
+    "        layout=\"constrained\",\n",
+    "    )\n",
+    "    axs = axs.flatten()\n",
+    "    plot_idx = 0\n",
+    "\n",
+    "    for model in MLIPEnum:\n",
+    "        fpath = DATA_DIR / f\"{model.name}.parquet\"\n",
+    "        if not fpath.exists():\n",
+    "            continue\n",
+    "\n",
+    "        if model.name not in select_models:\n",
+    "            continue\n",
+    "\n",
+    "        df = pd.read_parquet(fpath)\n",
+    "        df[\"eigval_min\"] = df[\"eigenvalues\"].apply(\n",
+    "            lambda x: x.min() if np.isreal(x).all() else thres\n",
+    "        )\n",
+    "        df[\"freq_min\"] = df[\"frequencies\"].apply(\n",
+    "            lambda x: x.min() if np.isreal(x).all() else thres\n",
+    "        )\n",
+    "        df[\"dyn_stab\"] = ~np.logical_or(\n",
+    "            df[\"eigval_min\"] < thres, df[\"freq_min\"] < thres\n",
+    "        )\n",
+    "\n",
+    "        arg = np.argsort(uids)\n",
+    "        uids_sorted = np.array(uids)[arg]\n",
+    "        stabilities_sorted = stabilities[arg]\n",
+    "\n",
+    "        sorted_df = (\n",
+    "            df[df[\"uid\"].isin(uids_sorted)].set_index(\"uid\").reindex(uids_sorted)\n",
+    "        )\n",
+    "        mask = ~(stabilities_sorted == None)\n",
+    "\n",
+    "        y_true = stabilities_sorted[mask].astype(\"int\")\n",
+    "        y_pred = sorted_df[\"dyn_stab\"][mask].fillna(-1).astype(\"int\")\n",
+    "        cm = confusion_matrix(y_true, y_pred, labels=[1, 0, -1])\n",
+    "\n",
+    "        ax = axs[plot_idx]\n",
+    "        ConfusionMatrixDisplay(\n",
+    "            cm, display_labels=[\"stable\", \"unstable\", \"missing\"]\n",
+    "        ).plot(ax=ax, cmap=\"Blues\", colorbar=False)\n",
+    "\n",
+    "        ax.set_title(model.name)\n",
+    "        ax.set_xlabel(\"Predicted\")\n",
+    "        ax.set_ylabel(\"True\")\n",
+    "        ax.set_xticks([0, 1, 2])\n",
+    "        ax.set_xticklabels([\"stable\", \"unstable\", \"missing\"])\n",
+    "        ax.set_yticks([0, 1, 2])\n",
+    "        ax.set_yticklabels([\"stable\", \"unstable\", \"missing\"])\n",
+    "\n",
+    "        plot_idx += 1\n",
+    "\n",
+    "    # Hide unused subplots\n",
+    "    for i in range(plot_idx, len(axs)):\n",
+    "        fig.delaxes(axs[i])\n",
+    "\n",
+    "    # plt.tight_layout()\n",
+    "    plt.savefig(\"c2db-confusion_matrices.pdf\", bbox_inches=\"tight\")\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "573b3c38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "from mlip_arena.models import MLIPEnum\n",
+    "\n",
+    "thres = -1e-7\n",
+    "\n",
+    "summary_df = pd.DataFrame(columns=[\"Model\", \"Stable F1\", \"Unstable F1\", \"Weighted F1\"])\n",
+    "\n",
+    "for model in MLIPEnum:\n",
+    "    fpath = DATA_DIR / f\"{model.name}.parquet\"\n",
+    "\n",
+    "    if not fpath.exists() or model.name not in select_models:\n",
+    "        # print(f\"File {fpath} does not exist\")\n",
+    "        continue\n",
+    "    df = pd.read_parquet(fpath)\n",
+    "\n",
+    "    df[\"eigval_min\"] = df[\"eigenvalues\"].apply(\n",
+    "        lambda x: x.min() if np.isreal(x).all() else thres\n",
+    "    )\n",
+    "    df[\"freq_min\"] = df[\"frequencies\"].apply(\n",
+    "        lambda x: x.min() if np.isreal(x).all() else thres\n",
+    "    )\n",
+    "    df[\"dyn_stab\"] = ~np.logical_or(df[\"eigval_min\"] < thres, df[\"freq_min\"] < thres)\n",
+    "\n",
+    "    arg = np.argsort(uids)\n",
+    "    uids = np.array(uids)[arg]\n",
+    "    stabilities = stabilities[arg]\n",
+    "\n",
+    "    sorted_df = df[df[\"uid\"].isin(uids)].sort_values(by=\"uid\")\n",
+    "\n",
+    "    # sorted_df = sorted_df.reindex(uids).reset_index()\n",
+    "    sorted_df = sorted_df.set_index(\"uid\").reindex(uids)  # .loc[uids].reset_index()\n",
+    "\n",
+    "    sorted_df = sorted_df.loc[uids]\n",
+    "    # mask = ~np.logical_or(sorted_df['dyn_stab'].isna().values, stabilities == None)\n",
+    "    mask = ~(stabilities == None)\n",
+    "\n",
+    "    y_true = stabilities[mask].astype(\"int\")\n",
+    "    y_pred = sorted_df[\"dyn_stab\"][mask].fillna(-1).astype(\"int\")\n",
+    "    cm = confusion_matrix(y_true, y_pred, labels=[1, 0, -1])\n",
+    "    # print(model)\n",
+    "    # print(cm)\n",
+    "    # print(classification_report(y_true, y_pred, labels=[1, 0], target_names=['stable', 'unstable'], digits=3, output_dict=False))\n",
+    "\n",
+    "    report = classification_report(\n",
+    "        y_true,\n",
+    "        y_pred,\n",
+    "        labels=[1, 0],\n",
+    "        target_names=[\"stable\", \"unstable\"],\n",
+    "        digits=3,\n",
+    "        output_dict=True,\n",
+    "    )\n",
+    "\n",
+    "    summary_df = pd.concat(\n",
+    "        [\n",
+    "            summary_df,\n",
+    "            pd.DataFrame(\n",
+    "                [\n",
+    "                    {\n",
+    "                        \"Model\": model.name,\n",
+    "                        \"Stable F1\": report[\"stable\"][\"f1-score\"],\n",
+    "                        \"Unstable F1\": report[\"unstable\"][\"f1-score\"],\n",
+    "                        \"Macro F1\": report[\"macro avg\"][\"f1-score\"],\n",
+    "                        # 'Micro F1': report['micro avg']['f1-score'],\n",
+    "                        \"Weighted F1\": report[\"weighted avg\"][\"f1-score\"],\n",
+    "                    }\n",
+    "                ]\n",
+    "            ),\n",
+    "        ],\n",
+    "        ignore_index=True,\n",
+    "    )\n",
+    "\n",
+    "    # break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "df660870",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary_df = summary_df.sort_values(by=[\"Macro F1\", \"Weighted F1\"], ascending=False)\n",
+    "summary_df.to_latex(\"c2db_summary_table.tex\", index=False, float_format=\"%.3f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "18f4a59b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import cm\n",
+    "\n",
+    "# Metrics and bar settings\n",
+    "metrics = [\"Stable F1\", \"Unstable F1\", \"Macro F1\", \"Weighted F1\"]\n",
+    "bar_width = 0.2\n",
+    "x = np.arange(len(summary_df))\n",
+    "\n",
+    "# Get Set2 colormap (as RGBA)\n",
+    "cmap = plt.get_cmap(\"tab20\")\n",
+    "colors = {metric: cmap(i) for i, metric in enumerate(metrics)}\n",
+    "\n",
+    "with plt.style.context(\"default\"):\n",
+    "    plt.rcParams.update(\n",
+    "        {\n",
+    "            \"font.size\": SMALL_SIZE,\n",
+    "            \"axes.titlesize\": MEDIUM_SIZE,\n",
+    "            \"axes.labelsize\": MEDIUM_SIZE,\n",
+    "            \"xtick.labelsize\": MEDIUM_SIZE,\n",
+    "            \"ytick.labelsize\": MEDIUM_SIZE,\n",
+    "            \"legend.fontsize\": SMALL_SIZE,\n",
+    "            \"figure.titlesize\": BIGGER_SIZE,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(4, 3), layout=\"constrained\")\n",
+    "\n",
+    "    # Bar positions\n",
+    "    positions = {\n",
+    "        \"Stable F1\": x - 1.5 * bar_width,\n",
+    "        \"Unstable F1\": x - 0.5 * bar_width,\n",
+    "        \"Macro F1\": x + 0.5 * bar_width,\n",
+    "        \"Weighted F1\": x + 1.5 * bar_width,\n",
+    "    }\n",
+    "\n",
+    "    # Plot each metric with assigned color\n",
+    "    for metric, pos in positions.items():\n",
+    "        ax.bar(\n",
+    "            pos, summary_df[metric], width=bar_width, label=metric, color=colors[metric]\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlabel(\"Model\")\n",
+    "    ax.set_ylabel(\"F1 Score\")\n",
+    "    # ax.set_title('F1 Scores by Model and Class')\n",
+    "    ax.set_xticks(x)\n",
+    "    ax.set_xticklabels(summary_df[\"Model\"], rotation=45, ha=\"right\")\n",
+    "    ax.legend(ncols=2, bbox_to_anchor=(0.5, 1), loc=\"upper center\", fontsize=SMALL_SIZE)\n",
+    "    # ax.legend(ncols=2, fontsize=SMALL_SIZE)\n",
+    "    ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "    plt.tight_layout()\n",
+    "    plt.ylim(0, 0.9)\n",
+    "    plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.6)\n",
+    "\n",
+    "    plt.savefig(\"c2db_f1_bar.pdf\", bbox_inches=\"tight\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c50f705",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mlip-arena",
+   "language": "python",
+   "name": "mlip-arena"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/c2db/c2db.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:caf58205692de480e06149ac43a437385f18e14582e7d9a8dab8b3cb5d4bd678
+size 70762496

examples/c2db/copy.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7fdc16667361b10bfb032862d5d0610c242d75cb88f7f3883c43a406b245e991
+size 21349

examples/c2db/run.py

@@ -0,0 +1,213 @@
+from itertools import product
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from dask.distributed import Client
+from dask_jobqueue import SLURMCluster
+from mlip_arena.models import MLIPEnum
+from mlip_arena.tasks import ELASTICITY, OPT, PHONON
+from mlip_arena.tasks.optimize import run as OPT
+from mlip_arena.tasks.utils import get_calculator
+from numpy import linalg as LA
+from prefect import flow, task
+from prefect_dask import DaskTaskRunner
+from tqdm.auto import tqdm
+
+from ase.db import connect
+
+select_models = [
+    "ALIGNN",
+    "CHGNet",
+    "M3GNet",
+    "MACE-MP(M)",
+    "MACE-MPA",
+    "MatterSim",
+    "ORBv2",
+    "SevenNet",
+]
+
+def elastic_tensor_to_voigt(C):
+    """
+    Convert a rank-4 (3x3x3x3) elastic tensor into a rank-2 (6x6) tensor using Voigt notation.
+
+    Parameters:
+    C (numpy.ndarray): A 3x3x3x3 elastic tensor.
+
+    Returns:
+    numpy.ndarray: A 6x6 elastic tensor in Voigt notation.
+    """
+    # voigt_map = {
+    #     (0, 0): 0, (1, 1): 1, (2, 2): 2,  # Normal components
+    #     (1, 2): 3, (2, 1): 3,  # Shear components
+    #     (0, 2): 4, (2, 0): 4,
+    #     (0, 1): 5, (1, 0): 5
+    # }
+    voigt_map = {
+        (0, 0): 0,
+        (1, 1): 1,
+        (2, 2): -1,  # Normal components
+        (1, 2): -1,
+        (2, 1): -1,  # Shear components
+        (0, 2): -1,
+        (2, 0): -1,
+        (0, 1): 2,
+        (1, 0): 2,
+    }
+
+    C_voigt = np.zeros((3, 3))
+
+    for i in range(3):
+        for j in range(3):
+            for k in range(3):
+                for l in range(3):
+                    alpha = voigt_map[(i, j)]
+                    beta = voigt_map[(k, l)]
+
+                    if alpha == -1 or beta == -1:
+                        continue
+
+                    factor = 1
+                    # if alpha in [3, 4, 5]:
+                    if alpha == 2:
+                        factor = factor * (2**0.5)
+                    if beta == 2:
+                        factor = factor * (2**0.5)
+
+                    C_voigt[alpha, beta] = C[i, j, k, l] * factor
+
+    return C_voigt
+
+
+# -
+
+
+@task
+def run_one(model, row):
+    if Path(f"{model.name}.pkl").exists():
+        df = pd.read_pickle(f"{model.name}.pkl")
+
+        # if row.key_value_pairs.get('uid', None) in df['uid'].unique():
+        #     pass
+    else:
+        df = pd.DataFrame(columns=["model", "uid", "eigenvalues", "frequencies"])
+
+    atoms = row.toatoms()
+    # print(data := row.key_value_pairs)
+
+    calc = get_calculator(model)
+
+    result_opt = OPT(
+        atoms,
+        calc,
+        optimizer="FIRE",
+        criterion=dict(fmax=0.05, steps=500),
+        symmetry=True,
+    )
+
+    atoms = result_opt["atoms"]
+
+    result_elastic = ELASTICITY(
+        atoms,
+        calc,
+        optimizer="FIRE",
+        criterion=dict(fmax=0.05, steps=500),
+        pre_relax=False,
+    )
+
+    elastic_tensor = elastic_tensor_to_voigt(result_elastic["elastic_tensor"])
+    eigenvalues, eigenvectors = LA.eig(elastic_tensor)
+
+    outdir = Path(f"{model.name}") / row.key_value_pairs.get(
+        "uid", atoms.get_chemical_formula()
+    )
+    outdir.mkdir(parents=True, exist_ok=True)
+
+    np.savez(outdir / "elastic.npz", tensor=elastic_tensor, eigenvalues=eigenvalues)
+
+    result_phonon = PHONON(
+        atoms,
+        calc,
+        supercell_matrix=(2, 2, 1),
+        outdir=outdir,
+    )
+
+    frequencies = result_phonon["phonon"].get_frequencies(q=(0, 0, 0))
+
+    new_row = pd.DataFrame(
+        [
+            {
+                "model": model.name,
+                "uid": row.key_value_pairs.get("uid", None),
+                "eigenvalues": eigenvalues,
+                "frequencies": frequencies,
+            }
+        ]
+    )
+
+    df = pd.concat([df, new_row], ignore_index=True)
+    df.drop_duplicates(subset=["model", "uid"], keep="last", inplace=True)
+
+    df.to_pickle(f"{model.name}.pkl")
+
+
+@flow
+def run_all():
+    import random
+
+    random.seed(0)
+
+    futures = []
+    with connect("c2db.db") as db:
+        random_indices = random.sample(range(1, len(db) + 1), 1000)
+        for row, model in tqdm(
+            product(db.select(filter=lambda r: r["id"] in random_indices), MLIPEnum)
+        ):
+            if model.name not in select_models:
+                continue
+            future = run_one.submit(model, row)
+            futures.append(future)
+    return [f.result(raise_on_failure=False) for f in futures]
+
+
+# +
+
+
+if __name__ == "__main__":
+    nodes_per_alloc = 1
+    gpus_per_alloc = 1
+    ntasks = 1
+
+    cluster_kwargs = dict(
+        cores=1,
+        memory="64 GB",
+        processes=1,
+        shebang="#!/bin/bash",
+        account="matgen",
+        walltime="00:30:00",
+        # job_cpu=128,
+        job_mem="0",
+        job_script_prologue=[
+            "source ~/.bashrc",
+            "module load python",
+            "source activate /pscratch/sd/c/cyrusyc/.conda/dev",
+        ],
+        job_directives_skip=["-n", "--cpus-per-task", "-J"],
+        job_extra_directives=[
+            "-J c2db",
+            "-q regular",
+            f"-N {nodes_per_alloc}",
+            "-C gpu",
+            f"-G {gpus_per_alloc}",
+        ],
+    )
+
+    cluster = SLURMCluster(**cluster_kwargs)
+    print(cluster.job_script())
+    cluster.adapt(minimum_jobs=25, maximum_jobs=50)
+    client = Client(cluster)
+    # -
+
+    run_all.with_options(
+        task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True
+    )()

examples/eos_bulk/CHGNet_processed.parquet

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examples/eos_bulk/M3GNet_processed.parquet

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examples/eos_bulk/MACE-MP(M)_processed.parquet

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examples/eos_bulk/MACE-MPA_processed.parquet

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examples/eos_bulk/MatterSim_processed.parquet

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examples/eos_bulk/ORBv2_processed.parquet

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examples/eos_bulk/SevenNet_processed.parquet

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examples/eos_bulk/analyze.py

@@ -0,0 +1,223 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from ase.db import connect
+from scipy import stats
+
+from mlip_arena.models import REGISTRY, MLIPEnum
+
+DATA_DIR = Path(__file__).parent.absolute()
+
+
+def load_wbm_structures():
+    """
+    Load the WBM structures from a ASE DB file.
+    """
+    with connect(DATA_DIR.parent / "wbm_structures.db") as db:
+        for row in db.select():
+            yield row.toatoms(add_additional_information=True)
+
+def gather_results():
+    for model in MLIPEnum:
+        if "eos_bulk" not in REGISTRY[model.name].get("gpu-tasks", []):
+            continue
+
+        if (DATA_DIR / f"{model.name}.parquet").exists():
+            continue
+
+        all_data = []
+
+        for atoms in load_wbm_structures():
+            fpath = Path(model.name) / f"{atoms.info['key_value_pairs']['wbm_id']}.pkl"
+            if not fpath.exists():
+                continue
+
+            all_data.append(pd.read_pickle(fpath))
+
+        df = pd.concat(all_data, ignore_index=True)
+        df.to_parquet(DATA_DIR / f"{model.name}.parquet")
+
+
+def summarize():
+    summary_table = pd.DataFrame(
+        columns=[
+            "model",
+            "energy-diff-flip-times",
+            "tortuosity",
+            "spearman-compression-energy",
+            "spearman-compression-derivative",
+            "spearman-tension-energy",
+            "missing",
+        ]
+    )
+
+
+    for model in MLIPEnum:
+        fpath = DATA_DIR / f"{model.name}.parquet"
+        if not fpath.exists():
+            continue
+        df_raw_results = pd.read_parquet(fpath)
+
+        df_analyzed = pd.DataFrame(
+            columns=[
+                "model",
+                "structure",
+                "formula",
+                "volume-ratio",
+                "energy-delta-per-atom",
+                "energy-diff-flip-times",
+                "energy-delta-per-volume-b0",
+                "tortuosity",
+                "spearman-compression-energy",
+                "spearman-compression-derivative",
+                "spearman-tension-energy",
+                "missing",
+            ]
+        )
+
+        for wbm_struct in load_wbm_structures():
+            structure_id = wbm_struct.info["key_value_pairs"]["wbm_id"]
+
+            try:
+                results = df_raw_results.loc[df_raw_results["id"] == structure_id]
+                b0 = results["b0"].values[0]
+                # vol0 = results["v0"].values[0]
+                results = results["eos"].values[0]
+                es = np.array(results["energies"])
+                vols = np.array(results["volumes"])
+
+                indices = np.argsort(vols)
+                vols = vols[indices]
+                es = es[indices]
+
+                imine = len(es) // 2
+                # min_center_val = np.min(es[imid - 1 : imid + 2])
+                # imine = np.where(es == min_center_val)[0][0]
+                emin = es[imine]
+                vol0 = vols[imine]
+
+                interpolated_volumes = [
+                    (vols[i] + vols[i + 1]) / 2 for i in range(len(vols) - 1)
+                ]
+                ediff = np.diff(es)
+                ediff_sign = np.sign(ediff)
+                mask = ediff_sign != 0
+                ediff = ediff[mask]
+                ediff_sign = ediff_sign[mask]
+                ediff_flip = np.diff(ediff_sign) != 0
+
+                etv = np.sum(np.abs(np.diff(es)))
+
+                data = {
+                    "model": model.name,
+                    "structure": structure_id,
+                    "formula": wbm_struct.get_chemical_formula(),
+                    "missing": False,
+                    "volume-ratio": vols / vol0,
+                    "energy-delta-per-atom": (es - emin) / len(wbm_struct),
+                    "energy-diff-flip-times": np.sum(ediff_flip).astype(int),
+                    "energy-delta-per-volume-b0": (es - emin) / (b0*vol0),
+                    "tortuosity": etv / (abs(es[0] - emin) + abs(es[-1] - emin)),
+                    "spearman-compression-energy": stats.spearmanr(
+                        vols[:imine], es[:imine]
+                    ).statistic,
+                    "spearman-compression-derivative": stats.spearmanr(
+                        interpolated_volumes[:imine], ediff[:imine]
+                    ).statistic,
+                    "spearman-tension-energy": stats.spearmanr(
+                        vols[imine:], es[imine:]
+                    ).statistic,
+                }
+
+            except Exception as e:
+                print(e)
+                data = {
+                    "model": model.name,
+                    "structure": structure_id,
+                    "formula": wbm_struct.get_chemical_formula(),
+                    "missing": True,
+                    "volume-ratio": None,
+                    "energy-delta-per-atom": None,
+                    "energy-delta-per-volume-b0": None,
+                    "energy-diff-flip-times": None,
+                    "tortuosity": None,
+                    "spearman-compression-energy": None,
+                    "spearman-compression-derivative": None,
+                    "spearman-tension-energy": None,
+                }
+
+            df_analyzed = pd.concat([df_analyzed, pd.DataFrame([data])], ignore_index=True)
+
+        df_analyzed.to_parquet(DATA_DIR / f"{model.name}_processed.parquet")
+        # json_fpath = DATA_DIR / f"EV_scan_analyzed_{model.name}.json"
+
+        # df_analyzed.to_json(json_fpath, orient="records")
+
+        valid_results = df_analyzed[df_analyzed["missing"] == False]
+
+        analysis_summary = {
+            "model": model.name,
+            "energy-diff-flip-times": valid_results["energy-diff-flip-times"].mean(),
+            "energy-diff-flip-times-std": valid_results["energy-diff-flip-times"].std(),
+            "tortuosity": valid_results["tortuosity"].mean(),
+            "tortuosity-std": valid_results["tortuosity"].std(),
+            "spearman-compression-energy": valid_results[
+                "spearman-compression-energy"
+            ].mean(),
+            "spearman-compression-energy-std": valid_results["spearman-compression-energy"].std(),
+            "spearman-compression-derivative": valid_results[
+                "spearman-compression-derivative"
+            ].mean(),
+            "spearman-compression-derivative-std": valid_results[
+                "spearman-compression-derivative"
+            ].std(),
+            "spearman-tension-energy": valid_results["spearman-tension-energy"].mean(),
+            "spearman-tension-energy-std": valid_results["spearman-tension-energy"].std(),
+            "missing": len(df_analyzed[df_analyzed["missing"] == True]),
+        }
+        summary_table = pd.concat(
+            [summary_table, pd.DataFrame([analysis_summary])], ignore_index=True
+        )
+
+
+    flip_rank = (
+        (summary_table["energy-diff-flip-times"] - 1)
+        .abs()
+        .rank(ascending=True, method="min")
+    )
+    tortuosity_rank = summary_table["tortuosity"].rank(ascending=True, method="min")
+    spearman_compression_energy_rank = summary_table["spearman-compression-energy"].rank(
+        method="min"
+    )
+    spearman_compression_derivative_rank = summary_table[
+        "spearman-compression-derivative"
+    ].rank(ascending=False, method="min")
+    spearman_tension_energy_rank = summary_table["spearman-tension-energy"].rank(
+        ascending=False, method="min"
+    )
+    missing_rank = summary_table["missing"].rank(ascending=True, method="min")
+
+    rank_aggr = (
+        flip_rank
+        + tortuosity_rank
+        + spearman_compression_energy_rank
+        + spearman_compression_derivative_rank
+        + spearman_tension_energy_rank
+        + missing_rank
+    )
+    rank = rank_aggr.rank(method="min")
+
+    summary_table.insert(1, "rank", rank.astype(int))
+    summary_table.insert(2, "rank-aggregation", rank_aggr.astype(int))
+    summary_table = summary_table.sort_values(by="rank", ascending=True)
+    summary_table = summary_table.reset_index(drop=True)
+
+    summary_table.to_csv(DATA_DIR / "summary.csv", index=False)
+    summary_table.to_latex(DATA_DIR / "summary.tex", index=False, float_format="%.3f")
+
+    return summary_table
+
+if __name__ == "__main__":
+    gather_results()
+    summarize()

examples/eos_bulk/eSEN_processed.parquet

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+size 393790

examples/eos_bulk/plot.py

@@ -0,0 +1,119 @@
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from ase.db import connect
+
+from mlip_arena.models import REGISTRY as MODELS
+
+DATA_DIR = Path(__file__).parent.absolute()
+
+# Use a qualitative color palette from matplotlib
+palette_name = "tab10"  # Better for distinguishing multiple lines
+color_sequence = plt.get_cmap(palette_name).colors
+
+valid_models = [
+    model
+    for model, metadata in MODELS.items()
+    if "eos_bulk" in metadata.get("gpu-tasks", [])
+]
+
+def load_wbm_structures():
+    """
+    Load the WBM structures from a ASE DB file.
+    """
+    with connect(DATA_DIR.parent / "wbm_structures.db") as db:
+        for row in db.select():
+            yield row.toatoms(add_additional_information=True)
+
+# # Collect valid models first
+# valid_models = []
+# for model_name in valid_models:
+#     fpath = DATA_DIR / f"{model_name}_processed.parquet"
+#     if fpath.exists():
+#         df = pd.read_parquet(fpath)
+#         if len(df) > 0:
+#             valid_models.append(model)
+
+# # Ensure we're showing all 8 models
+# if len(valid_models) < 8:
+#     print(f"Warning: Only found {len(valid_models)} valid models instead of 8")
+
+# Set up the grid layout
+n_models = len(valid_models)
+n_cols = 4  # Use 4 columns
+n_rows = (n_models + n_cols - 1) // n_cols  # Ceiling division to get required rows
+
+# Create figure with enough space for all subplots
+fig = plt.figure(
+    figsize=(6, 1.25 * n_rows),  # Wider for better readability
+    constrained_layout=True,  # Better than tight_layout for this case
+)
+
+# Create grid of subplots
+axes = []
+for i in range(n_models):
+    ax = plt.subplot(n_rows, n_cols, i+1)
+    axes.append(ax)
+
+SMALL_SIZE = 6
+MEDIUM_SIZE = 8
+LARGE_SIZE = 10
+
+# Fill in the subplots with data
+for i, model_name in enumerate(valid_models):
+    fpath = DATA_DIR / f"{model_name}_processed.parquet"
+    df = pd.read_parquet(fpath)
+
+    ax = axes[i]
+    valid_structures = []
+
+    for j, (_, row) in enumerate(df.iterrows()):
+        structure_id = row["structure"]
+        formula = row.get("formula", "")
+        if isinstance(row["volume-ratio"], (list, np.ndarray)) and isinstance(
+            row["energy-delta-per-volume-b0"], (list, np.ndarray)
+        ):
+            vol_strain = row["volume-ratio"]
+            energy_delta = row["energy-delta-per-volume-b0"]
+            color = color_sequence[j % len(color_sequence)]
+            ax.plot(
+                vol_strain,
+                energy_delta,
+                color=color,
+                linewidth=1,
+                alpha=0.9,
+            )
+            valid_structures.append(structure_id)
+
+    # Set subplot title
+    ax.set_title(f"{model_name} ({len(valid_structures)})", fontsize=MEDIUM_SIZE)
+    
+    # Only add y-label to leftmost plots (those with index divisible by n_cols)
+    if i % n_cols == 0:
+        ax.set_ylabel("$\\frac{\\Delta E}{B V_0}$", fontsize=MEDIUM_SIZE)
+    else:
+        ax.set_ylabel("")
+    
+    # Only add x-label to bottom row plots
+    # Check if this plot is in the bottom row
+    is_bottom_row = (i // n_cols) == (n_rows - 1) or (i >= n_models - n_cols)
+    if is_bottom_row:
+        ax.set_xlabel("$V/V_0$", fontsize=MEDIUM_SIZE)
+    else:
+        ax.set_xlabel("")
+    
+    ax.set_ylim(-0.02, 0.1)  # Consistent y-limits
+    ax.axvline(x=1, linestyle="--", color="gray", alpha=0.7)
+    ax.tick_params(axis="both", which="major", labelsize=MEDIUM_SIZE)
+
+# Make sure all subplots share the x and y limits
+for ax in axes:
+    ax.set_xlim(0.8, 1.2)  # Adjust these as needed
+    ax.set_ylim(-0.02, 0.1)
+
+# Save the figure with all plots
+plt.savefig(DATA_DIR / "eos-bulk-grid.png", dpi=300, bbox_inches="tight")
+plt.savefig(DATA_DIR / "eos-bulk-grid.pdf", bbox_inches="tight")
+# plt.show()

examples/eos_bulk/summary.csv

@@ -1,9 +1,9 @@
-model,rank,rank-aggregation,energy-diff-flip-times,tortuosity,spearman-compression-energy,spearman-compression-derivative,spearman-tension-energy,missing
-MACE-MPA,1,7,1.0370741482965933,1.005455197941088,-0.9993684338373716,0.9963320580555048,0.993186372745491,2
-eSEN,2,15,1.042211055276382,1.0082267858369258,-0.9993299832495811,0.9968570123343992,0.9920968478757424,5
-MACE-MP(M),3,20,1.042211055276382,1.008986842539345,-0.999329983249581,0.9941160347190496,0.9915857612939804,5
-MatterSim,4,22,1.045135406218656,1.0060900449752808,-0.99734962463147,0.9927904926901917,0.9880977115916667,3
-CHGNet,5,27,1.1053159478435306,1.014753469076796,-0.9964985866690981,0.9929971733381963,0.9866417434120545,3
-SevenNet,6,32,1.1093279839518555,1.0186969977862483,-0.9981277164827815,0.9889121911188109,0.9859580417030127,3
-M3GNet,7,38,1.1748743718592964,1.0175007963267957,-0.9963209989340641,0.9897426526572255,0.9801690217498693,5
-ORBv2,8,48,1.3162134944612287,1.0374718753890275,-0.9918459519667977,0.9701425127407,0.9637462235649547,7
+model,rank,rank-aggregation,energy-diff-flip-times,tortuosity,spearman-compression-energy,spearman-compression-derivative,spearman-tension-energy,missing,energy-diff-flip-times-std,tortuosity-std,spearman-compression-energy-std,spearman-compression-derivative-std,spearman-tension-energy-std
+MACE-MPA,1,7,1.0370741482965933,1.005455197941088,-0.9993684338373716,0.9963320580555048,0.993186372745491,2,0.28260902337559207,0.05365793240575371,0.01247051827833709,0.03852356148675327,0.07744103059608153
+eSEN,2,15,1.042211055276382,1.0082267858369258,-0.9993299832495811,0.9968570123343992,0.9920968478757424,5,0.31435657463218236,0.09009343693321628,0.012254373724862471,0.03683033142639347,0.07346152527758068
+MACE-MP(M),3,20,1.042211055276382,1.008986842539345,-0.999329983249581,0.9941160347190496,0.9915857612939804,5,0.3448779209525529,0.1291612188691875,0.011378760248143813,0.059100297945675236,0.0879944289437058
+MatterSim,4,22,1.045135406218656,1.0060900449752808,-0.99734962463147,0.9927904926901917,0.9880977115916667,3,0.376211439097473,0.055231139063835144,0.03888149868978045,0.07797796889169765,0.11523169167576831
+CHGNet,5,27,1.1053159478435306,1.014753469076796,-0.9964985866690981,0.9929971733381963,0.9866417434120545,3,0.5395426308257233,0.12255069852201543,0.051058628207987275,0.05245296840977506,0.11650035215147045
+SevenNet,6,32,1.1093279839518555,1.0186969977862483,-0.9981277164827815,0.9889121911188109,0.9859580417030127,3,0.5552625647497746,0.2749956370938698,0.025627387238441334,0.07657439969351316,0.11715556163493943
+M3GNet,7,38,1.1748743718592964,1.0175007963267957,-0.9963209989340641,0.9897426526572255,0.9801690217498693,5,0.6755070078112404,0.1488462321310986,0.05166730302469224,0.06468954475570607,0.1332400556108672
+ORBv2,8,48,1.3162134944612287,1.0374718753890275,-0.9918459519667977,0.9701425127407,0.9637462235649547,7,0.8699212994733451,0.2149179445701606,0.08208261674781654,0.1315974423719716,0.19758814985102582

examples/eos_bulk/summary.tex

@@ -1,14 +1,14 @@
-\begin{tabular}{lrrrrrrrl}
+\begin{tabular}{lrrrrrrrlrrrrr}
 \toprule
-model & rank & rank-aggregation & energy-diff-flip-times & tortuosity & spearman-compression-energy & spearman-compression-derivative & spearman-tension-energy & missing \\
+model & rank & rank-aggregation & energy-diff-flip-times & tortuosity & spearman-compression-energy & spearman-compression-derivative & spearman-tension-energy & missing & energy-diff-flip-times-std & tortuosity-std & spearman-compression-energy-std & spearman-compression-derivative-std & spearman-tension-energy-std \\
 \midrule
-MACE-MPA & 1 & 7 & 1.037074 & 1.005455 & -0.999368 & 0.996332 & 0.993186 & 2 \\
-eSEN & 2 & 15 & 1.042211 & 1.008227 & -0.999330 & 0.996857 & 0.992097 & 5 \\
-MACE-MP(M) & 3 & 20 & 1.042211 & 1.008987 & -0.999330 & 0.994116 & 0.991586 & 5 \\
-MatterSim & 4 & 22 & 1.045135 & 1.006090 & -0.997350 & 0.992790 & 0.988098 & 3 \\
-CHGNet & 5 & 27 & 1.105316 & 1.014753 & -0.996499 & 0.992997 & 0.986642 & 3 \\
-SevenNet & 6 & 32 & 1.109328 & 1.018697 & -0.998128 & 0.988912 & 0.985958 & 3 \\
-M3GNet & 7 & 38 & 1.174874 & 1.017501 & -0.996321 & 0.989743 & 0.980169 & 5 \\
-ORBv2 & 8 & 48 & 1.316213 & 1.037472 & -0.991846 & 0.970143 & 0.963746 & 7 \\
+MACE-MPA & 1 & 7 & 1.037 & 1.005 & -0.999 & 0.996 & 0.993 & 2 & 0.283 & 0.054 & 0.012 & 0.039 & 0.077 \\
+eSEN & 2 & 15 & 1.042 & 1.008 & -0.999 & 0.997 & 0.992 & 5 & 0.314 & 0.090 & 0.012 & 0.037 & 0.073 \\
+MACE-MP(M) & 3 & 20 & 1.042 & 1.009 & -0.999 & 0.994 & 0.992 & 5 & 0.345 & 0.129 & 0.011 & 0.059 & 0.088 \\
+MatterSim & 4 & 22 & 1.045 & 1.006 & -0.997 & 0.993 & 0.988 & 3 & 0.376 & 0.055 & 0.039 & 0.078 & 0.115 \\
+CHGNet & 5 & 27 & 1.105 & 1.015 & -0.996 & 0.993 & 0.987 & 3 & 0.540 & 0.123 & 0.051 & 0.052 & 0.117 \\
+SevenNet & 6 & 32 & 1.109 & 1.019 & -0.998 & 0.989 & 0.986 & 3 & 0.555 & 0.275 & 0.026 & 0.077 & 0.117 \\
+M3GNet & 7 & 38 & 1.175 & 1.018 & -0.996 & 0.990 & 0.980 & 5 & 0.676 & 0.149 & 0.052 & 0.065 & 0.133 \\
+ORBv2 & 8 & 48 & 1.316 & 1.037 & -0.992 & 0.970 & 0.964 & 7 & 0.870 & 0.215 & 0.082 & 0.132 & 0.198 \\
 \bottomrule
 \end{tabular}

examples/mof/structures/dac/SGU-29.cif

@@ -0,0 +1,995 @@
+data_sgu-29b_1\(2)
+_audit_creation_date              2025-02-06
+_audit_creation_method            'Materials Studio'
+_symmetry_space_group_name_H-M    'P1'
+_symmetry_Int_Tables_number       1
+_symmetry_cell_setting            triclinic
+loop_
+_symmetry_equiv_pos_as_xyz
+  x,y,z
+_cell_length_a                    20.8200
+_cell_length_b                    20.8190
+_cell_length_c                    14.6970
+_cell_angle_alpha                 90.0000
+_cell_angle_beta                  110.7300
+_cell_angle_gamma                 90.0000
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_U_iso_or_equiv
+_atom_site_adp_type
+_atom_site_occupancy
+Si1    Si    0.24909   0.06259   0.24949   0.00000  Uiso   1.00
+O2     O     0.37297   0.87246   0.00024   0.00000  Uiso   1.00
+O3     O     0.31082   0.10699   0.31523   0.00000  Uiso   1.00
+O4     O     0.43778   0.35840   0.18606   0.00000  Uiso   1.00
+O5     O     0.53049   0.76657   0.18649   0.00000  Uiso   1.00
+O6     O     0.43915   0.85680   0.18418   0.00000  Uiso   1.00
+O7     O     0.18809   0.10887   0.18562   0.00000  Uiso   1.00
+O8     O     0.21990   0.01612   0.31305   0.00000  Uiso   1.00
+O9     O     0.27771   0.01820   0.18331   0.00000  Uiso   1.00
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+O12    O     0.49794   0.25289   0.50055   0.00000  Uiso   1.00
+O13    O     0.12201   0.12220   0.99919   0.00000  Uiso   1.00
+Cu14   Cu    0.37361   0.37328   0.50133   0.00000  Uiso   1.00
+Si15   Si    0.25762   0.96125   0.10339   0.00000  Uiso   1.00
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+Na37   Na    0.64365   0.69124   0.31112   0.00000  Uiso   1.00
+Si38   Si    0.74909   0.56259   0.24949   0.00000  Uiso   1.00
+O39    O     0.87297   0.37246   0.00024   0.00000  Uiso   1.00
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+O50    O     0.62201   0.62220   0.99919   0.00000  Uiso   1.00
+Cu51   Cu    0.87361   0.87328   0.50133   0.00000  Uiso   1.00
+Si52   Si    0.75762   0.46125   0.10339   0.00000  Uiso   1.00
+Si53   Si    0.86206   0.35698   0.10386   0.00000  Uiso   1.00
+Si54   Si    0.68420   0.51989   0.39513   0.00000  Uiso   1.00
+Si55   Si    0.93514   0.76839   0.39631   0.00000  Uiso   1.00
+Si56   Si    0.00622   0.21051   0.10427   0.00000  Uiso   1.00
+Si57   Si    0.92111   0.91440   0.10420   0.00000  Uiso   1.00
+Si58   Si    0.61160   0.60500   0.10374   0.00000  Uiso   1.00
+Si59   Si    0.83078   0.66403   0.39571   0.00000  Uiso   1.00
+O60    O     0.89390   0.83193   0.39620   0.00000  Uiso   1.00
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+Na72   Na    0.26421   0.31238   0.30670   0.00000  Uiso   1.00
+Na73   Na    0.11250   0.93394   0.18653   0.00000  Uiso   1.00
+Na74   Na    0.14365   0.19124   0.31112   0.00000  Uiso   1.00
+Na75   Na    0.98990   0.56290   0.20950   0.00000  Uiso   1.00
+Si76   Si    0.75091   0.06259   0.25051   0.00000  Uiso   1.00
+O77    O     0.62703   0.87246   0.49976   0.00000  Uiso   1.00
+O78    O     0.68918   0.10699   0.18477   0.00000  Uiso   1.00
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+O88    O     0.87799   0.12220   0.50081   0.00000  Uiso   1.00
+Cu89   Cu    0.62639   0.37328   0.99867   0.00000  Uiso   1.00
+Si90   Si    0.74238   0.96125   0.39661   0.00000  Uiso   1.00
+Si91   Si    0.63794   0.85698   0.39614   0.00000  Uiso   1.00
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+Na112  Na    0.35635   0.69124   0.18888   0.00000  Uiso   1.00
+Na113  Na    0.51010   0.06290   0.29050   0.00000  Uiso   1.00
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+Cu127  Cu    0.12639   0.87328   0.99867   0.00000  Uiso   1.00
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+Si130  Si    0.31580   0.51989   0.10487   0.00000  Uiso   1.00
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+Na150  Na    0.85635   0.19124   0.18888   0.00000  Uiso   1.00
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+O163   O     0.87799   0.87780   0.00081   0.00000  Uiso   1.00
+Cu164  Cu    0.62639   0.62672   0.49867   0.00000  Uiso   1.00
+Si165  Si    0.74238   0.03875   0.89661   0.00000  Uiso   1.00
+Si166  Si    0.63794   0.14302   0.89614   0.00000  Uiso   1.00
+Si167  Si    0.81580   0.98011   0.60487   0.00000  Uiso   1.00
+Si168  Si    0.56486   0.73161   0.60369   0.00000  Uiso   1.00
+Si169  Si    0.49378   0.28949   0.89573   0.00000  Uiso   1.00
+Si170  Si    0.57889   0.58560   0.89580   0.00000  Uiso   1.00
+Si171  Si    0.88840   0.89500   0.89626   0.00000  Uiso   1.00
+Si172  Si    0.66922   0.83597   0.60429   0.00000  Uiso   1.00
+O173   O     0.60610   0.66807   0.60380   0.00000  Uiso   1.00
+O174   O     0.67190   0.07970   0.86730   0.00000  Uiso   1.00
+O175   O     0.51220   0.54930   0.89550   0.00000  Uiso   1.00
+O176   O     0.61350   0.79460   0.63340   0.00000  Uiso   1.00
+O177   O     0.80640   0.07990   0.89690   0.00000  Uiso   1.00
+O178   O     0.92490   0.96150   0.89610   0.00000  Uiso   1.00
+O179   O     0.86950   0.03960   0.63490   0.00000  Uiso   1.00
+O180   O     0.67910   0.20690   0.89560   0.00000  Uiso   1.00
+O181   O     0.73330   0.79520   0.60460   0.00000  Uiso   1.00
+O182   O     0.85240   0.91346   0.60550   0.00000  Uiso   1.00
+O183   O     0.56000   0.32590   0.89490   0.00000  Uiso   1.00
+O184   O     0.42700   0.33610   0.86630   0.00000  Uiso   1.00
+Na185  Na    0.23579   0.18762   0.69330   0.00000  Uiso   1.00
+Na186  Na    0.38750   0.56606   0.81347   0.00000  Uiso   1.00
+Na187  Na    0.35635   0.30876   0.68888   0.00000  Uiso   1.00
+Si188  Si    0.25091   0.43741   0.75051   0.00000  Uiso   1.00
+O189   O     0.12703   0.62754   0.99976   0.00000  Uiso   1.00
+O190   O     0.18918   0.39301   0.68477   0.00000  Uiso   1.00
+O191   O     0.06222   0.14160   0.81394   0.00000  Uiso   1.00
+O192   O     0.96951   0.73343   0.81351   0.00000  Uiso   1.00
+O193   O     0.06085   0.64320   0.81582   0.00000  Uiso   1.00
+O194   O     0.31191   0.39113   0.81438   0.00000  Uiso   1.00
+O195   O     0.28010   0.48388   0.68695   0.00000  Uiso   1.00
+O196   O     0.22229   0.48180   0.81669   0.00000  Uiso   1.00
+O197   O     0.02779   0.23194   0.68373   0.00000  Uiso   1.00
+O198   O     0.25338   0.50209   0.00114   0.00000  Uiso   1.00
+O199   O     0.00206   0.24711   0.49945   0.00000  Uiso   1.00
+O200   O     0.37799   0.37780   0.00081   0.00000  Uiso   1.00
+Cu201  Cu    0.12639   0.12672   0.49867   0.00000  Uiso   1.00
+Si202  Si    0.24238   0.53875   0.89661   0.00000  Uiso   1.00
+Si203  Si    0.13794   0.64302   0.89614   0.00000  Uiso   1.00
+Si204  Si    0.31580   0.48011   0.60487   0.00000  Uiso   1.00
+Si205  Si    0.06486   0.23161   0.60369   0.00000  Uiso   1.00
+Si206  Si    0.99378   0.78949   0.89573   0.00000  Uiso   1.00
+Si207  Si    0.07889   0.08560   0.89580   0.00000  Uiso   1.00
+Si208  Si    0.38840   0.39500   0.89626   0.00000  Uiso   1.00
+Si209  Si    0.16922   0.33597   0.60429   0.00000  Uiso   1.00
+O210   O     0.10610   0.16807   0.60380   0.00000  Uiso   1.00
+O211   O     0.17190   0.57970   0.86730   0.00000  Uiso   1.00
+O212   O     0.01220   0.04930   0.89550   0.00000  Uiso   1.00
+O213   O     0.11350   0.29460   0.63340   0.00000  Uiso   1.00
+O214   O     0.30640   0.57990   0.89690   0.00000  Uiso   1.00
+O215   O     0.42490   0.46150   0.89610   0.00000  Uiso   1.00
+O216   O     0.36950   0.53960   0.63490   0.00000  Uiso   1.00
+O217   O     0.17910   0.70690   0.89560   0.00000  Uiso   1.00
+O218   O     0.23330   0.29520   0.60460   0.00000  Uiso   1.00
+O219   O     0.35240   0.41346   0.60550   0.00000  Uiso   1.00
+O220   O     0.06000   0.82590   0.89490   0.00000  Uiso   1.00
+O221   O     0.92700   0.83610   0.86630   0.00000  Uiso   1.00
+Na222  Na    0.73579   0.68762   0.69330   0.00000  Uiso   1.00
+Na223  Na    0.88750   0.06606   0.81347   0.00000  Uiso   1.00
+Na224  Na    0.85635   0.80876   0.68888   0.00000  Uiso   1.00
+Na225  Na    0.01010   0.43710   0.79050   0.00000  Uiso   1.00
+Si226  Si    0.24909   0.93741   0.74949   0.00000  Uiso   1.00
+O227   O     0.37297   0.12754   0.50024   0.00000  Uiso   1.00
+O228   O     0.31082   0.89301   0.81523   0.00000  Uiso   1.00
+O229   O     0.43778   0.64160   0.68606   0.00000  Uiso   1.00
+O230   O     0.53049   0.23343   0.68649   0.00000  Uiso   1.00
+O231   O     0.43915   0.14320   0.68418   0.00000  Uiso   1.00
+O232   O     0.18809   0.89113   0.68562   0.00000  Uiso   1.00
+O233   O     0.21990   0.98388   0.81305   0.00000  Uiso   1.00
+O234   O     0.27771   0.98180   0.68331   0.00000  Uiso   1.00
+O235   O     0.47221   0.73194   0.81627   0.00000  Uiso   1.00
+O236   O     0.24662   0.00209   0.49886   0.00000  Uiso   1.00
+O237   O     0.49794   0.74711   0.00055   0.00000  Uiso   1.00
+O238   O     0.12201   0.87780   0.49919   0.00000  Uiso   1.00
+Cu239  Cu    0.37361   0.62672   0.00133   0.00000  Uiso   1.00
+Si240  Si    0.25762   0.03875   0.60339   0.00000  Uiso   1.00
+Si241  Si    0.36206   0.14302   0.60386   0.00000  Uiso   1.00
+Si242  Si    0.18420   0.98011   0.89513   0.00000  Uiso   1.00
+Si243  Si    0.43514   0.73161   0.89631   0.00000  Uiso   1.00
+Si244  Si    0.50622   0.28949   0.60427   0.00000  Uiso   1.00
+Si245  Si    0.42111   0.58560   0.60420   0.00000  Uiso   1.00
+Si246  Si    0.11160   0.89500   0.60374   0.00000  Uiso   1.00
+Si247  Si    0.33078   0.83597   0.89571   0.00000  Uiso   1.00
+O248   O     0.39390   0.66807   0.89620   0.00000  Uiso   1.00
+O249   O     0.32810   0.07970   0.63270   0.00000  Uiso   1.00
+O250   O     0.48780   0.54930   0.60450   0.00000  Uiso   1.00
+O251   O     0.38650   0.79460   0.86660   0.00000  Uiso   1.00
+O252   O     0.19360   0.07990   0.60310   0.00000  Uiso   1.00
+O253   O     0.07510   0.96150   0.60390   0.00000  Uiso   1.00
+O254   O     0.13050   0.03960   0.86510   0.00000  Uiso   1.00
+O255   O     0.32090   0.20690   0.60440   0.00000  Uiso   1.00
+O256   O     0.26670   0.79520   0.89540   0.00000  Uiso   1.00
+O257   O     0.14760   0.91346   0.89450   0.00000  Uiso   1.00
+O258   O     0.44000   0.32590   0.60510   0.00000  Uiso   1.00
+O259   O     0.57300   0.33610   0.63370   0.00000  Uiso   1.00
+Na260  Na    0.76421   0.18762   0.80670   0.00000  Uiso   1.00
+Na261  Na    0.61250   0.56606   0.68653   0.00000  Uiso   1.00
+Na262  Na    0.64365   0.30876   0.81112   0.00000  Uiso   1.00
+Na263  Na    0.48990   0.93710   0.70950   0.00000  Uiso   1.00
+Si264  Si    0.74909   0.43741   0.74949   0.00000  Uiso   1.00
+O265   O     0.87297   0.62754   0.50024   0.00000  Uiso   1.00
+O266   O     0.81082   0.39301   0.81523   0.00000  Uiso   1.00
+O267   O     0.93778   0.14160   0.68606   0.00000  Uiso   1.00
+O268   O     0.03049   0.73343   0.68649   0.00000  Uiso   1.00
+O269   O     0.93915   0.64320   0.68418   0.00000  Uiso   1.00
+O270   O     0.68809   0.39113   0.68562   0.00000  Uiso   1.00
+O271   O     0.71990   0.48388   0.81305   0.00000  Uiso   1.00
+O272   O     0.77771   0.48180   0.68331   0.00000  Uiso   1.00
+O273   O     0.97221   0.23194   0.81627   0.00000  Uiso   1.00
+O274   O     0.74662   0.50209   0.49886   0.00000  Uiso   1.00
+O275   O     0.99794   0.24711   0.00055   0.00000  Uiso   1.00
+O276   O     0.62201   0.37780   0.49919   0.00000  Uiso   1.00
+Cu277  Cu    0.87361   0.12672   0.00133   0.00000  Uiso   1.00
+Si278  Si    0.75762   0.53875   0.60339   0.00000  Uiso   1.00
+Si279  Si    0.86206   0.64302   0.60386   0.00000  Uiso   1.00
+Si280  Si    0.68420   0.48011   0.89513   0.00000  Uiso   1.00
+Si281  Si    0.93514   0.23161   0.89631   0.00000  Uiso   1.00
+Si282  Si    0.00622   0.78949   0.60427   0.00000  Uiso   1.00
+Si283  Si    0.92111   0.08560   0.60420   0.00000  Uiso   1.00
+Si284  Si    0.61160   0.39500   0.60374   0.00000  Uiso   1.00
+Si285  Si    0.83078   0.33597   0.89571   0.00000  Uiso   1.00
+O286   O     0.89390   0.16807   0.89620   0.00000  Uiso   1.00
+O287   O     0.82810   0.57970   0.63270   0.00000  Uiso   1.00
+O288   O     0.98780   0.04930   0.60450   0.00000  Uiso   1.00
+O289   O     0.88650   0.29460   0.86660   0.00000  Uiso   1.00
+O290   O     0.69360   0.57990   0.60310   0.00000  Uiso   1.00
+O291   O     0.57510   0.46150   0.60390   0.00000  Uiso   1.00
+O292   O     0.63050   0.53960   0.86510   0.00000  Uiso   1.00
+O293   O     0.82090   0.70690   0.60440   0.00000  Uiso   1.00
+O294   O     0.76670   0.29520   0.89540   0.00000  Uiso   1.00
+O295   O     0.64760   0.41346   0.89450   0.00000  Uiso   1.00
+O296   O     0.94000   0.82590   0.60510   0.00000  Uiso   1.00
+O297   O     0.07300   0.83610   0.63370   0.00000  Uiso   1.00
+Na298  Na    0.26421   0.68762   0.80670   0.00000  Uiso   1.00
+Na299  Na    0.11250   0.06606   0.68653   0.00000  Uiso   1.00
+Na300  Na    0.14365   0.80876   0.81112   0.00000  Uiso   1.00
+Si301  Si    0.50000   0.31328   0.25000   0.00000  Uiso   1.00
+Si302  Si    0.50000   0.81163   0.25000   0.00000  Uiso   1.00
+Na303  Na    0.50000   0.56280   0.25000   0.00000  Uiso   1.00
+Si304  Si    0.00000   0.81328   0.25000   0.00000  Uiso   1.00
+Si305  Si    0.00000   0.31163   0.25000   0.00000  Uiso   1.00
+Na306  Na    0.00000   0.06280   0.25000   0.00000  Uiso   1.00
+Si307  Si    0.50000   0.68672   0.75000   0.00000  Uiso   1.00
+Si308  Si    0.50000   0.18837   0.75000   0.00000  Uiso   1.00
+Na309  Na    0.50000   0.43720   0.75000   0.00000  Uiso   1.00
+Si310  Si   -0.00000   0.18672   0.75000   0.00000  Uiso   1.00
+Si311  Si   -0.00000   0.68837   0.75000   0.00000  Uiso   1.00
+Na312  Na   -0.00000   0.93720   0.75000   0.00000  Uiso   1.00
+Cu313  Cu    0.25000   0.75000  -0.00000   0.00000  Uiso   1.00
+Cu314  Cu    0.75000   0.75000   0.50000   0.00000  Uiso   1.00
+Cu315  Cu    0.25000   0.25000   0.50000   0.00000  Uiso   1.00
+Cu316  Cu    0.75000   0.25000   0.00000   0.00000  Uiso   1.00
+Cu317  Cu    0.50000   0.50000  -0.00000   0.00000  Uiso   1.00
+Cu318  Cu    0.00000   0.00000   0.00000   0.00000  Uiso   1.00
+Cu319  Cu    0.50000   0.50000   0.50000   0.00000  Uiso   1.00
+Cu320  Cu    0.00000   0.00000   0.50000   0.00000  Uiso   1.00
+loop_
+_geom_bond_atom_site_label_1
+_geom_bond_atom_site_label_2
+_geom_bond_distance
+_geom_bond_site_symmetry_2
+_ccdc_geom_bond_type
+Si1    O3      1.600   .     S
+Si1    O7      1.606   .     S
+Si1    O8      1.606   .     S
+Si1    O9      1.602   .     S
+O2     Si16    1.649   .     S
+O2     Si247   1.659   1_554 S
+O3     Si22    1.623   .     S
+O4     Si20    1.622   .     S
+O4     Si301   1.607   .     S
+O5     Si19    1.626   .     S
+O5     Si302   1.604   .     S
+O6     Si16    1.621   .     S
+O6     Si302   1.599   .     S
+O7     Si21    1.623   .     S
+O8     Si17    1.627   .     S
+O9     Si15    1.616   1_545 S
+O10    Si18    1.619   .     S
+O10    Si301   1.603   .     S
+O11    Si15    1.657   1_556 S
+O11    Si242   1.658   .     S
+O12    Si18    1.656   .     S
+O12    Si244   1.658   .     S
+O13    Si21    1.665   1_556 S
+O13    Si207   1.652   .     S
+Cu14   O23     1.941   .     S
+Cu14   O219    1.928   .     S
+Cu14   Na187   3.199   .     S
+Cu14   O258    1.928   .     S
+Cu14   Na111   3.141   .     S
+Cu14   O140    1.935   .     S
+Cu14   Na72    3.220   .     S
+Si15   O11     1.657   1_554 S
+Si15   O9      1.616   1_565 S
+Si15   O24     1.618   .     S
+Si15   O27     1.583   .     S
+Si16   O24     1.622   .     S
+Si16   O30     1.584   .     S
+Si17   O29     1.622   1_545 S
+Si17   O32     1.582   .     S
+Si17   O236    1.658   .     S
+Si18   O23     1.577   .     S
+Si18   O26     1.620   .     S
+Si19   O33     1.577   .     S
+Si19   O34     1.624   .     S
+Si19   O237    1.658   .     S
+Si20   O25     1.579   .     S
+Si20   O200    1.652   .     S
+Si20   O142    1.618   .     S
+Si21   O13     1.665   1_554 S
+Si21   O28     1.580   .     S
+Si21   O71     1.611   .     S
+Si22   O26     1.619   .     S
+Si22   O31     1.580   .     S
+Si22   O227    1.659   .     S
+O23    Na72    2.583   .     S
+O23    Na111   2.427   .     S
+O25    Na36    2.470   .     S
+O25    Cu317   1.937   .     S
+O27    Cu127   1.935   1_554 S
+O27    Na73    2.430   .     S
+O27    Na110   2.595   .     S
+O28    Cu318   1.931   .     S
+O28    Na73    2.480   1_545 S
+O29    Si17    1.622   1_565 S
+O29    Si133   1.618   .     S
+O29    Na73    2.576   .     S
+O30    Cu313   1.933   .     S
+O30    Na110   2.578   .     S
+O30    Na112   2.436   .     S
+O31    Cu315   1.936   .     S
+O31    Na72    2.583   .     S
+O31    Na74    2.435   .     S
+O32    Cu201   1.928   .     S
+O32    Na74    2.488   .     S
+O33    Cu239   1.928   .     S
+O33    Na112   2.489   .     S
+O34    Na37    2.564   .     S
+O34    Si58    1.611   .     S
+Na35   Na37    3.575   .     S
+Na35   Cu51    3.220   .     S
+Na35   O60     2.583   .     S
+Na35   O102    2.595   .     S
+Na35   O105    2.578   .     S
+Na35   O68     2.583   .     S
+Na35   Na149   3.581   .     S
+Na35   Cu314   3.231   .     S
+Na36   Cu317   3.217   .     S
+Na36   Cu89    3.141   1_554 S
+Na36   O98     2.427   .     S
+Na36   O64     2.430   .     S
+Na36   O65     2.480   .     S
+Na36   O66     2.576   .     S
+Na36   Na148   3.581   .     S
+Na37   Cu164   3.199   .     S
+Na37   O105    2.436   .     S
+Na37   O68     2.435   .     S
+Na37   O69     2.488   .     S
+Na37   O108    2.489   .     S
+Na37   Cu314   3.123   .     S
+Si38   O40     1.600   .     S
+Si38   O44     1.606   .     S
+Si38   O45     1.606   .     S
+Si38   O46     1.602   .     S
+O39    Si53    1.649   .     S
+O39    Si285   1.659   1_554 S
+O40    Si59    1.623   .     S
+O41    Si57    1.622   .     S
+O41    Si304   1.607   1_655 S
+O42    Si56    1.626   .     S
+O42    Si305   1.604   .     S
+O43    Si53    1.621   .     S
+O43    Si305   1.599   1_655 S
+O44    Si58    1.623   .     S
+O45    Si54    1.627   .     S
+O46    Si52    1.616   .     S
+O47    Si55    1.619   .     S
+O47    Si304   1.603   1_655 S
+O48    Si52    1.657   1_556 S
+O48    Si280   1.658   .     S
+O49    Si55    1.656   .     S
+O49    Si282   1.658   1_655 S
+O50    Si58    1.665   1_556 S
+O50    Si170   1.652   .     S
+Cu51   O60     1.941   .     S
+Cu51   O182    1.928   .     S
+Cu51   Na224   3.199   .     S
+Cu51   O296    1.928   .     S
+Cu51   Na149   3.141   .     S
+Cu51   O102    1.935   .     S
+Si52   O48     1.657   1_554 S
+Si52   O61     1.618   .     S
+Si52   O64     1.583   .     S
+Si53   O61     1.622   .     S
+Si53   O67     1.584   .     S
+Si54   O66     1.622   .     S
+Si54   O69     1.582   .     S
+Si54   O274    1.658   .     S
+Si55   O60     1.577   .     S
+Si55   O63     1.620   .     S
+Si56   O70     1.577   1_455 S
+Si56   O71     1.624   .     S
+Si56   O275    1.658   1_455 S
+Si57   O62     1.579   .     S
+Si57   O163    1.652   .     S
+Si57   O104    1.618   .     S
+Si58   O50     1.665   1_554 S
+Si58   O65     1.580   .     S
+Si59   O63     1.619   .     S
+Si59   O68     1.580   .     S
+Si59   O265    1.659   .     S
+O60    Na149   2.427   .     S
+O62    Na73    2.470   1_655 S
+O62    Cu318   1.937   1_665 S
+O64    Cu89    1.935   1_554 S
+O64    Na148   2.595   .     S
+O65    Cu317   1.931   .     S
+O66    Si95    1.618   .     S
+O67    Cu316   1.933   .     S
+O67    Na148   2.578   .     S
+O67    Na150   2.436   .     S
+O68    Cu314   1.936   .     S
+O69    Cu164   1.928   .     S
+O70    Si56    1.577   1_655 S
+O70    Cu277   1.928   .     S
+O70    Na150   2.489   .     S
+O71    Na74    2.564   .     S
+Na72   Na74    3.575   .     S
+Na72   O140    2.595   .     S
+Na72   O143    2.578   .     S
+Na72   Na111   3.581   .     S
+Na72   Cu315   3.231   .     S
+Na73   O62     2.470   1_455 S
+Na73   Cu318   3.217   1_565 S
+Na73   Cu127   3.141   1_554 S
+Na73   O136    2.427   .     S
+Na73   O28     2.480   1_565 S
+Na73   Na110   3.581   .     S
+Na74   Cu201   3.199   .     S
+Na74   O143    2.436   .     S
+Na74   O146    2.489   .     S
+Na74   Cu315   3.123   .     S
+Si76   O78     1.600   .     S
+Si76   O82     1.606   .     S
+Si76   O83     1.606   .     S
+Si76   O84     1.602   .     S
+O77    Si91    1.649   .     S
+O77    Si172   1.659   .     S
+O78    Si97    1.623   .     S
+O79    Si95    1.622   .     S
+O79    Si301   1.607   .     S
+O80    Si94    1.626   .     S
+O80    Si302   1.604   .     S
+O81    Si91    1.621   .     S
+O81    Si302   1.599   .     S
+O82    Si96    1.623   .     S
+O83    Si92    1.627   .     S
+O84    Si90    1.616   1_545 S
+O85    Si93    1.619   .     S
+O85    Si301   1.603   .     S
+O86    Si90    1.657   .     S
+O86    Si167   1.658   .     S
+O87    Si93    1.656   1_556 S
+O87    Si169   1.658   .     S
+O88    Si96    1.665   .     S
+O88    Si283   1.652   .     S
+Cu89   O98     1.941   1_556 S
+Cu89   O295    1.928   .     S
+Cu89   Na262   3.199   .     S
+Cu89   O183    1.928   .     S
+Cu89   Na36    3.141   1_556 S
+Cu89   O64     1.935   1_556 S
+Cu89   Na148   3.220   1_556 S
+Si90   O84     1.616   1_565 S
+Si90   O99     1.618   .     S
+Si90   O102    1.583   .     S
+Si91   O99     1.622   .     S
+Si91   O105    1.584   .     S
+Si92   O104    1.622   1_545 S
+Si92   O107    1.582   .     S
+Si92   O161    1.658   .     S
+Si93   O87     1.656   1_554 S
+Si93   O98     1.577   .     S
+Si93   O101    1.620   .     S
+Si94   O108    1.577   .     S
+Si94   O109    1.624   .     S
+Si94   O162    1.658   .     S
+Si95   O100    1.579   .     S
+Si95   O276    1.652   .     S
+Si96   O103    1.580   .     S
+Si96   O147    1.611   .     S
+Si97   O101    1.619   .     S
+Si97   O106    1.580   .     S
+Si97   O152    1.659   1_554 S
+O98    Cu89    1.941   1_554 S
+O98    Na148   2.583   .     S
+O100   Na111   2.470   .     S
+O100   Cu319   1.937   .     S
+O102   Na149   2.430   .     S
+O103   Cu320   1.931   1_655 S
+O103   Na149   2.480   1_545 S
+O104   Si92    1.622   1_565 S
+O104   Na149   2.576   .     S
+O105   Cu314   1.933   .     S
+O106   Cu316   1.936   .     S
+O106   Na148   2.583   .     S
+O106   Na150   2.435   .     S
+O107   Cu277   1.928   .     S
+O107   Na150   2.488   .     S
+O108   Cu164   1.928   .     S
+O109   Na112   2.564   .     S
+O109   Si134   1.611   .     S
+Na110  Na112   3.575   .     S
+Na110  Cu127   3.220   1_554 S
+Na110  O136    2.583   .     S
+Na110  O144    2.583   .     S
+Na110  Cu313   3.231   .     S
+Na111  Cu319   3.217   .     S
+Na111  O140    2.430   .     S
+Na111  O141    2.480   .     S
+Na111  O142    2.576   .     S
+Na112  Cu239   3.199   .     S
+Na112  O144    2.435   .     S
+Na112  O145    2.488   .     S
+Na112  Cu313   3.123   .     S
+Si114  O116    1.600   .     S
+Si114  O120    1.606   .     S
+Si114  O121    1.606   .     S
+Si114  O122    1.602   .     S
+O115   Si129   1.649   .     S
+O115   Si209   1.659   .     S
+O116   Si135   1.623   .     S
+O117   Si133   1.622   .     S
+O117   Si304   1.607   .     S
+O118   Si132   1.626   .     S
+O118   Si305   1.604   1_655 S
+O119   Si129   1.621   .     S
+O119   Si305   1.599   .     S
+O120   Si134   1.623   .     S
+O121   Si130   1.627   .     S
+O122   Si128   1.616   .     S
+O123   Si131   1.619   .     S
+O123   Si304   1.603   .     S
+O124   Si128   1.657   .     S
+O124   Si204   1.658   .     S
+O125   Si131   1.656   1_556 S
+O125   Si206   1.658   1_455 S
+O126   Si134   1.665   .     S
+O126   Si245   1.652   .     S
+Cu127  O136    1.941   1_556 S
+Cu127  O257    1.928   .     S
+Cu127  Na300   3.199   .     S
+Cu127  O220    1.928   .     S
+Cu127  Na73    3.141   1_556 S
+Cu127  O27     1.935   1_556 S
+Cu127  Na110   3.220   1_556 S
+Si128  O137    1.618   .     S
+Si128  O140    1.583   .     S
+Si129  O137    1.622   .     S
+Si129  O143    1.584   .     S
+Si130  O142    1.622   .     S
+Si130  O145    1.582   .     S
+Si130  O198    1.658   .     S
+Si131  O125    1.656   1_554 S
+Si131  O136    1.577   .     S
+Si131  O139    1.620   .     S
+Si132  O146    1.577   1_655 S
+Si132  O147    1.624   .     S
+Si132  O199    1.658   1_655 S
+Si133  O138    1.579   .     S
+Si133  O238    1.652   .     S
+Si134  O141    1.580   .     S
+Si135  O139    1.619   .     S
+Si135  O144    1.580   .     S
+Si135  O189    1.659   1_554 S
+O136   Cu127   1.941   1_554 S
+O138   Na149   2.470   1_455 S
+O138   Cu320   1.937   1_565 S
+O141   Cu319   1.931   .     S
+O143   Cu315   1.933   .     S
+O144   Cu313   1.936   .     S
+O145   Cu239   1.928   .     S
+O146   Si132   1.577   1_455 S
+O146   Cu201   1.928   .     S
+O147   Na150   2.564   .     S
+Na148  Na150   3.575   .     S
+Na148  Cu89    3.220   1_554 S
+Na148  Cu316   3.231   .     S
+Na149  O138    2.470   1_655 S
+Na149  Cu320   3.217   1_665 S
+Na149  O103    2.480   1_565 S
+Na150  Cu277   3.199   .     S
+Na150  Cu316   3.123   .     S
+Si151  O153    1.600   .     S
+Si151  O157    1.606   .     S
+Si151  O158    1.606   .     S
+Si151  O159    1.602   .     S
+O152   Si166   1.649   .     S
+O152   Si97    1.659   1_556 S
+O153   Si172   1.623   .     S
+O154   Si170   1.622   .     S
+O154   Si307   1.607   .     S
+O155   Si169   1.626   .     S
+O155   Si308   1.604   .     S
+O156   Si166   1.621   .     S
+O156   Si308   1.599   .     S
+O157   Si171   1.623   .     S
+O158   Si167   1.627   .     S
+O159   Si165   1.616   1_565 S
+O160   Si168   1.619   .     S
+O160   Si307   1.603   .     S
+O161   Si165   1.657   1_554 S
+O162   Si168   1.656   .     S
+O163   Si171   1.665   1_554 S
+Cu164  O173    1.941   .     S
+Cu164  Na261   3.141   .     S
+Cu164  O290    1.935   .     S
+Cu164  Na222   3.220   .     S
+Si165  O161    1.657   1_556 S
+Si165  O159    1.616   1_545 S
+Si165  O174    1.618   .     S
+Si165  O177    1.583   .     S
+Si166  O174    1.622   .     S
+Si166  O180    1.584   .     S
+Si167  O179    1.622   1_565 S
+Si167  O182    1.582   .     S
+Si168  O173    1.577   .     S
+Si168  O176    1.620   .     S
+Si169  O183    1.577   .     S
+Si169  O184    1.624   .     S
+Si170  O175    1.579   .     S
+Si170  O292    1.618   .     S
+Si171  O163    1.665   1_556 S
+Si171  O178    1.580   .     S
+Si171  O221    1.611   .     S
+Si172  O176    1.619   .     S
+Si172  O181    1.580   .     S
+O173   Na222   2.583   .     S
+O173   Na261   2.427   .     S
+O175   Na186   2.470   .     S
+O175   Cu317   1.937   1_556 S
+O177   Cu277   1.935   1_556 S
+O177   Na223   2.430   .     S
+O177   Na260   2.595   .     S
+O178   Cu318   1.931   1_666 S
+O178   Na223   2.480   1_565 S
+O179   Si167   1.622   1_545 S
+O179   Si283   1.618   .     S
+O179   Na223   2.576   .     S
+O180   Cu316   1.933   1_556 S
+O180   Na260   2.578   .     S
+O180   Na262   2.436   .     S
+O181   Cu314   1.936   .     S
+O181   Na222   2.583   .     S
+O181   Na224   2.435   .     S
+O182   Na224   2.488   .     S
+O183   Na262   2.489   .     S
+O184   Na187   2.564   .     S
+O184   Si208   1.611   .     S
+Na185  Na187   3.575   .     S
+Na185  Cu201   3.220   .     S
+Na185  O210    2.583   .     S
+Na185  O252    2.595   .     S
+Na185  O255    2.578   .     S
+Na185  O218    2.583   .     S
+Na185  Na299   3.581   .     S
+Na185  Cu315   3.231   .     S
+Na186  Cu317   3.217   1_556 S
+Na186  Cu239   3.141   1_556 S
+Na186  O248    2.427   .     S
+Na186  O214    2.430   .     S
+Na186  O215    2.480   .     S
+Na186  O216    2.576   .     S
+Na186  Na298   3.581   .     S
+Na187  O255    2.436   .     S
+Na187  O218    2.435   .     S
+Na187  O219    2.488   .     S
+Na187  O258    2.489   .     S
+Na187  Cu315   3.123   .     S
+Si188  O190    1.600   .     S
+Si188  O194    1.606   .     S
+Si188  O195    1.606   .     S
+Si188  O196    1.602   .     S
+O189   Si203   1.649   .     S
+O189   Si135   1.659   1_556 S
+O190   Si209   1.623   .     S
+O191   Si207   1.622   .     S
+O191   Si310   1.607   .     S
+O192   Si206   1.626   .     S
+O192   Si311   1.604   1_655 S
+O193   Si203   1.621   .     S
+O193   Si311   1.599   .     S
+O194   Si208   1.623   .     S
+O195   Si204   1.627   .     S
+O196   Si202   1.616   .     S
+O197   Si205   1.619   .     S
+O197   Si310   1.603   .     S
+O198   Si202   1.657   1_554 S
+O199   Si205   1.656   .     S
+O199   Si132   1.658   1_455 S
+O200   Si208   1.665   1_554 S
+Cu201  O210    1.941   .     S
+Cu201  Na299   3.141   .     S
+Cu201  O252    1.935   .     S
+Si202  O198    1.657   1_556 S
+Si202  O211    1.618   .     S
+Si202  O214    1.583   .     S
+Si203  O211    1.622   .     S
+Si203  O217    1.584   .     S
+Si204  O216    1.622   .     S
+Si204  O219    1.582   .     S
+Si205  O210    1.577   .     S
+Si205  O213    1.620   .     S
+Si206  O220    1.577   1_655 S
+Si206  O221    1.624   .     S
+Si206  O125    1.658   1_655 S
+Si207  O212    1.579   .     S
+Si207  O254    1.618   .     S
+Si208  O200    1.665   1_556 S
+Si208  O215    1.580   .     S
+Si209  O213    1.619   .     S
+Si209  O218    1.580   .     S
+O210   Na299   2.427   .     S
+O212   Na223   2.470   1_455 S
+O212   Cu318   1.937   1_556 S
+O214   Cu239   1.935   1_556 S
+O214   Na298   2.595   .     S
+O215   Cu317   1.931   1_556 S
+O216   Si245   1.618   .     S
+O217   Cu313   1.933   1_556 S
+O217   Na298   2.578   .     S
+O217   Na300   2.436   .     S
+O218   Cu315   1.936   .     S
+O220   Si206   1.577   1_455 S
+O220   Na300   2.489   .     S
+O221   Na224   2.564   .     S
+Na222  Na224   3.575   .     S
+Na222  O290    2.595   .     S
+Na222  O293    2.578   .     S
+Na222  Na261   3.581   .     S
+Na222  Cu314   3.231   .     S
+Na223  O212    2.470   1_655 S
+Na223  Cu318   3.217   1_656 S
+Na223  Cu277   3.141   1_556 S
+Na223  O286    2.427   .     S
+Na223  O178    2.480   1_545 S
+Na223  Na260   3.581   .     S
+Na224  O293    2.436   .     S
+Na224  O296    2.489   .     S
+Na224  Cu314   3.123   .     S
+Si226  O228    1.600   .     S
+Si226  O232    1.606   .     S
+Si226  O233    1.606   .     S
+Si226  O234    1.602   .     S
+O227   Si241   1.649   .     S
+O228   Si247   1.623   .     S
+O229   Si245   1.622   .     S
+O229   Si307   1.607   .     S
+O230   Si244   1.626   .     S
+O230   Si308   1.604   .     S
+O231   Si241   1.621   .     S
+O231   Si308   1.599   .     S
+O232   Si246   1.623   .     S
+O233   Si242   1.627   .     S
+O234   Si240   1.616   1_565 S
+O235   Si243   1.619   .     S
+O235   Si307   1.603   .     S
+O236   Si240   1.657   .     S
+O237   Si243   1.656   1_554 S
+O238   Si246   1.665   .     S
+Cu239  O248    1.941   1_554 S
+Cu239  Na186   3.141   1_554 S
+Cu239  O214    1.935   1_554 S
+Cu239  Na298   3.220   1_554 S
+Si240  O234    1.616   1_545 S
+Si240  O249    1.618   .     S
+Si240  O252    1.583   .     S
+Si241  O249    1.622   .     S
+Si241  O255    1.584   .     S
+Si242  O254    1.622   1_565 S
+Si242  O257    1.582   .     S
+Si243  O237    1.656   1_556 S
+Si243  O248    1.577   .     S
+Si243  O251    1.620   .     S
+Si244  O258    1.577   .     S
+Si244  O259    1.624   .     S
+Si245  O250    1.579   .     S
+Si246  O253    1.580   .     S
+Si246  O297    1.611   .     S
+Si247  O251    1.619   .     S
+Si247  O256    1.580   .     S
+Si247  O2      1.659   1_556 S
+O248   Cu239   1.941   1_556 S
+O248   Na298   2.583   .     S
+O250   Na261   2.470   .     S
+O250   Cu319   1.937   .     S
+O252   Na299   2.430   .     S
+O253   Cu320   1.931   1_565 S
+O253   Na299   2.480   1_565 S
+O254   Si242   1.622   1_545 S
+O254   Na299   2.576   .     S
+O255   Cu315   1.933   .     S
+O256   Cu313   1.936   1_556 S
+O256   Na298   2.583   .     S
+O256   Na300   2.435   .     S
+O257   Na300   2.488   .     S
+O259   Na262   2.564   .     S
+O259   Si284   1.611   .     S
+Na260  Na262   3.575   .     S
+Na260  Cu277   3.220   1_556 S
+Na260  O286    2.583   .     S
+Na260  O294    2.583   .     S
+Na260  Cu316   3.231   1_556 S
+Na261  Cu319   3.217   .     S
+Na261  O290    2.430   .     S
+Na261  O291    2.480   .     S
+Na261  O292    2.576   .     S
+Na262  O294    2.435   .     S
+Na262  O295    2.488   .     S
+Na262  Cu316   3.123   1_556 S
+Si264  O266    1.600   .     S
+Si264  O270    1.606   .     S
+Si264  O271    1.606   .     S
+Si264  O272    1.602   .     S
+O265   Si279   1.649   .     S
+O266   Si285   1.623   .     S
+O267   Si283   1.622   .     S
+O267   Si310   1.607   1_655 S
+O268   Si282   1.626   .     S
+O268   Si311   1.604   .     S
+O269   Si279   1.621   .     S
+O269   Si311   1.599   1_655 S
+O270   Si284   1.623   .     S
+O271   Si280   1.627   .     S
+O272   Si278   1.616   .     S
+O273   Si281   1.619   .     S
+O273   Si310   1.603   1_655 S
+O274   Si278   1.657   .     S
+O275   Si281   1.656   1_554 S
+O275   Si56    1.658   1_655 S
+O276   Si284   1.665   .     S
+Cu277  O286    1.941   1_554 S
+Cu277  Na223   3.141   1_554 S
+Cu277  O177    1.935   1_554 S
+Cu277  Na260   3.220   1_554 S
+Si278  O287    1.618   .     S
+Si278  O290    1.583   .     S
+Si279  O287    1.622   .     S
+Si279  O293    1.584   .     S
+Si280  O292    1.622   .     S
+Si280  O295    1.582   .     S
+Si281  O275    1.656   1_556 S
+Si281  O286    1.577   .     S
+Si281  O289    1.620   .     S
+Si282  O296    1.577   1_455 S
+Si282  O297    1.624   .     S
+Si282  O49     1.658   1_455 S
+Si283  O288    1.579   .     S
+Si284  O291    1.580   .     S
+Si285  O289    1.619   .     S
+Si285  O294    1.580   .     S
+Si285  O39     1.659   1_556 S
+O286   Cu277   1.941   1_556 S
+O288   Na299   2.470   1_655 S
+O288   Cu320   1.937   1_655 S
+O291   Cu319   1.931   .     S
+O293   Cu314   1.933   .     S
+O294   Cu316   1.936   1_556 S
+O296   Si282   1.577   1_655 S
+O297   Na300   2.564   .     S
+Na298  Na300   3.575   .     S
+Na298  Cu239   3.220   1_556 S
+Na298  Cu313   3.231   1_556 S
+Na299  O288    2.470   1_455 S
+Na299  Cu320   3.217   .     S
+Na299  O253    2.480   1_545 S
+Na300  Cu313   3.123   1_556 S
+Si304  O41     1.607   1_455 S
+Si304  O47     1.603   1_455 S
+Si305  O118    1.604   1_455 S
+Si305  O43     1.599   1_455 S
+Si310  O267    1.607   1_455 S
+Si310  O273    1.603   1_455 S
+Si311  O192    1.604   1_455 S
+Si311  O269    1.599   1_455 S
+Cu313  O217    1.933   1_554 S
+Cu313  O256    1.936   1_554 S
+Cu313  Na298   3.231   1_554 S
+Cu313  Na300   3.123   1_554 S
+Cu316  O180    1.933   1_554 S
+Cu316  O294    1.936   1_554 S
+Cu316  Na260   3.231   1_554 S
+Cu316  Na262   3.123   1_554 S
+Cu317  O175    1.937   1_554 S
+Cu317  Na186   3.217   1_554 S
+Cu317  O215    1.931   1_554 S
+Cu318  O62     1.937   1_445 S
+Cu318  O212    1.937   1_554 S
+Cu318  Na73    3.217   1_545 S
+Cu318  Na223   3.217   1_454 S
+Cu318  O178    1.931   1_444 S
+Cu320  O138    1.937   1_545 S
+Cu320  O288    1.937   1_455 S
+Cu320  Na149   3.217   1_445 S
+Cu320  O103    1.931   1_455 S
+Cu320  O253    1.931   1_545 S

examples/stability/pressure.ipynb

@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from ase import units\n",
+    "from dask.distributed import Client\n",
+    "from dask_jobqueue import SLURMCluster\n",
+    "from dotenv import load_dotenv\n",
+    "from prefect import flow, task\n",
+    "from prefect_dask import DaskTaskRunner\n",
+    "\n",
+    "from mlip_arena.models import REGISTRY, MLIPEnum\n",
+    "from mlip_arena.tasks.md import run as MD\n",
+    "from mlip_arena.tasks.stability.input import get_atoms_from_db\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "HF_TOKEN = os.environ.get(\"HF_TOKEN\", None)\n",
+    "MP_API_KEY = os.environ.get(\"MP_API_KEY\", None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "nodes_per_alloc = 1\n",
+    "gpus_per_alloc = 4\n",
+    "ntasks = 1\n",
+    "\n",
+    "cluster_kwargs = dict(\n",
+    "    cores=1,\n",
+    "    memory=\"64 GB\",\n",
+    "    processes=1,\n",
+    "    shebang=\"#!/bin/bash\",\n",
+    "    account=\"matgen\",\n",
+    "    walltime=\"03:00:00\",\n",
+    "    # job_cpu=128,\n",
+    "    job_mem=\"0\",\n",
+    "    job_script_prologue=[\n",
+    "        \"source ~/.bashrc\",\n",
+    "        \"module load python\",\n",
+    "        \"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\",\n",
+    "    ],\n",
+    "    job_directives_skip=[\"-n\", \"--cpus-per-task\", \"-J\"],\n",
+    "    job_extra_directives=[\n",
+    "        \"-J stability-npt\",\n",
+    "        \"-q preempt\",\n",
+    "        \"--time-min=00:30:00\",\n",
+    "        \"--comment=12:00:00\",\n",
+    "        f\"-N {nodes_per_alloc}\",\n",
+    "        \"-C gpu\",\n",
+    "        f\"-G {gpus_per_alloc}\",\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "cluster = SLURMCluster(**cluster_kwargs)\n",
+    "print(cluster.job_script())\n",
+    "cluster.adapt(minimum_jobs=5, maximum_jobs=10)\n",
+    "client = Client(cluster)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlip_arena.tasks.utils import get_calculator\n",
+    "\n",
+    "selected_models = [\n",
+    "    \"MACE-MP(M)\",\n",
+    "    \"CHGNet\",\n",
+    "    \"M3GNet\",\n",
+    "    \"MatterSim\",\n",
+    "    \"eqV2(OMat)\",\n",
+    "    \"MACE-MPA\",\n",
+    "    \"ORBv2\",\n",
+    "    \"SevenNet\",\n",
+    "    \"ALIGNN\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "@task\n",
+    "def run_one(\n",
+    "    atoms,\n",
+    "    model,\n",
+    "):\n",
+    "    result = MD.with_options(\n",
+    "        timeout_seconds=600,\n",
+    "        retries=2,\n",
+    "        refresh_cache=True\n",
+    "    )(\n",
+    "        atoms=atoms,\n",
+    "        calculator=get_calculator(\n",
+    "            model.name,\n",
+    "            calculator_kwargs=None,\n",
+    "        ),\n",
+    "        ensemble=\"npt\",\n",
+    "        dynamics=\"nose-hoover\",\n",
+    "        time_step=None,\n",
+    "        dynamics_kwargs=dict(\n",
+    "            ttime=25 * units.fs, pfactor=((75 * units.fs) ** 2) * 1e2 * units.GPa\n",
+    "        ),\n",
+    "        total_time=1e4,  # 5e4, # fs\n",
+    "        temperature=[300, 3000],\n",
+    "        pressure=[0, 5e2 * units.GPa],  # 500 GPa / 10 ps = 50 GPa / 1 ps\n",
+    "        traj_file=f\"{REGISTRY[model.name]['family']}/{model.name}_{atoms.info.get('material_id', 'random')}_{atoms.get_chemical_formula()}_npt.traj\",\n",
+    "        traj_interval=10,\n",
+    "    )\n",
+    "\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "@flow\n",
+    "def compress():\n",
+    "    futures = []\n",
+    "    # To download the database automatically, `huggingface_hub login` or provide HF_TOKEN\n",
+    "    for atoms in get_atoms_from_db(\"random-mixture.db\", force_download=False):\n",
+    "        for model in MLIPEnum:\n",
+    "            if model.name not in selected_models:\n",
+    "                continue\n",
+    "\n",
+    "            if \"stability\" not in REGISTRY[model.name][\"gpu-tasks\"]:\n",
+    "                continue\n",
+    "\n",
+    "            try:\n",
+    "                future = run_one.with_options(\n",
+    "                    timeout_seconds=600, retries=2, refresh_cache=False\n",
+    "                ).submit(atoms.copy(), model)\n",
+    "                futures.append(future)\n",
+    "            except:\n",
+    "                continue\n",
+    "\n",
+    "    return [future.result(raise_on_failure=False) for future in futures]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compress.with_options(\n",
+    "    task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True\n",
+    ")()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "NERSC Python",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/stability/temperature.ipynb

@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from ase import units\n",
+    "from dask.distributed import Client\n",
+    "from dask_jobqueue import SLURMCluster\n",
+    "from dotenv import load_dotenv\n",
+    "from prefect import flow, task\n",
+    "from prefect_dask import DaskTaskRunner\n",
+    "\n",
+    "from mlip_arena.models import REGISTRY, MLIPEnum\n",
+    "from mlip_arena.tasks.md import run as MD\n",
+    "from mlip_arena.tasks.stability.input import get_atoms_from_db\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "HF_TOKEN = os.environ.get(\"HF_TOKEN\", None)\n",
+    "MP_API_KEY = os.environ.get(\"MP_API_KEY\", None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "nodes_per_alloc = 1\n",
+    "gpus_per_alloc = 4\n",
+    "ntasks = 1\n",
+    "\n",
+    "cluster_kwargs = dict(\n",
+    "    cores=1,\n",
+    "    memory=\"64 GB\",\n",
+    "    processes=1,\n",
+    "    shebang=\"#!/bin/bash\",\n",
+    "    account=\"matgen\",\n",
+    "    walltime=\"04:00:00\",\n",
+    "    # job_cpu=128,\n",
+    "    job_mem=\"0\",\n",
+    "    job_script_prologue=[\n",
+    "        \"source ~/.bashrc\",\n",
+    "        \"module load python\",\n",
+    "        \"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\",\n",
+    "    ],\n",
+    "    job_directives_skip=[\"-n\", \"--cpus-per-task\", \"-J\"],\n",
+    "    job_extra_directives=[\n",
+    "        \"-J stability-nvt\",\n",
+    "        \"-q preempt\",\n",
+    "        \"--time-min=00:30:00\",\n",
+    "        \"--comment=12:00:00\",\n",
+    "        f\"-N {nodes_per_alloc}\",\n",
+    "        \"-C gpu\",\n",
+    "        f\"-G {gpus_per_alloc}\",\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "cluster = SLURMCluster(**cluster_kwargs)\n",
+    "print(cluster.job_script())\n",
+    "cluster.adapt(minimum_jobs=10, maximum_jobs=50)\n",
+    "client = Client(cluster)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from prefect.cache_policies import INPUTS, TASK_SOURCE\n",
+    "from prefect.futures import wait\n",
+    "\n",
+    "from mlip_arena.tasks.utils import get_calculator\n",
+    "\n",
+    "selected_models = [\n",
+    "    \"MACE-MP(M)\",\n",
+    "    \"CHGNet\",\n",
+    "    \"M3GNet\",\n",
+    "    \"MatterSim\",\n",
+    "    \"eqV2(OMat)\",\n",
+    "    \"MACE-MPA\",\n",
+    "    \"ORBv2\",\n",
+    "    \"SevenNet\",\n",
+    "    \"ALIGNN\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "@task(cache_policy=TASK_SOURCE + INPUTS)\n",
+    "def run_one(\n",
+    "    atoms,\n",
+    "    model,\n",
+    "):\n",
+    "    try:\n",
+    "        result = MD.with_options(\n",
+    "            # timeout_seconds=600,\n",
+    "            # retries=1,\n",
+    "            refresh_cache=True\n",
+    "        )(\n",
+    "            atoms=atoms,\n",
+    "            calculator=get_calculator(\n",
+    "                model.name,\n",
+    "                calculator_kwargs=None,\n",
+    "            ),\n",
+    "            ensemble=\"nvt\",\n",
+    "            dynamics=\"nose-hoover\",\n",
+    "            time_step=None,\n",
+    "            dynamics_kwargs=dict(\n",
+    "                ttime=25 * units.fs,\n",
+    "                # pfactor=((75 * units.fs) ** 2) * 1e2 * units.GPa\n",
+    "            ),\n",
+    "            total_time=1e4,  # 5e4, # fs\n",
+    "            temperature=[300, 3000],\n",
+    "            pressure=None,\n",
+    "            traj_file=f\"{REGISTRY[model.name]['family']}/{model.name}_{atoms.info.get('material_id', 'random')}_{atoms.get_chemical_formula()}_nvt.traj\",\n",
+    "            traj_interval=10,\n",
+    "        )\n",
+    "    except Exception as e:\n",
+    "        print(e)\n",
+    "        return e\n",
+    "\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "@flow\n",
+    "def heat():\n",
+    "    futures = []\n",
+    "    # To download the database automatically, `huggingface_hub login` or provide HF_TOKEN\n",
+    "    for atoms in get_atoms_from_db(\"random-mixture.db\", force_download=False):\n",
+    "        for model in MLIPEnum:\n",
+    "            if model.name not in selected_models:\n",
+    "                continue\n",
+    "\n",
+    "            future = run_one.with_options(\n",
+    "                timeout_seconds=600, retries=2, refresh_cache=False\n",
+    "            ).submit(atoms.copy(), model)\n",
+    "            futures.append(future)\n",
+    "\n",
+    "    wait(futures)\n",
+    "\n",
+    "    return [\n",
+    "        f.result(timeout=None, raise_on_failure=False)\n",
+    "        for f in futures\n",
+    "        if f.state.is_completed()\n",
+    "    ]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heat.with_options(\n",
+    "    task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True\n",
+    ")()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mlip-arena",
+   "language": "python",
+   "name": "mlip-arena"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/vacancy_migration/Table-A1-fcc.csv

@@ -0,0 +1,58 @@
+symbol,cohesive_energy,K,K',volume_per_atom,e_vacform,e_vacmig
+Ac,3.70,21.63,2.03,45.37,1.26,0.45
+Ag,2.49,84.11,5.15,17.87,0.68,0.70
+Al,3.43,76.90,4.23,16.47,0.61,0.58
+Ar,0.02,1.60,3.45,45.00,0.01,0.06
+Au,2.99,136.19,7.45,18.07,0.40,0.53
+Ba,1.87,8.01,2.81,64.13,1.09,0.36
+Be,3.64,116.96,3.90,7.88,-0.06,0.75
+Ca,1.91,16.82,1.70,42.17,1.13,0.47
+Cd,0.74,41.66,5.71,22.60,0.30,0.23
+Ce,4.58,37.65,4.10,26.10,1.30,0.54
+Co,4.92,251.54,5.17,10.30,1.84,1.45
+Co_mag,5.11,209.89,5.07,10.90,1.79,1.01
+Cs,0.70,2.40,2.36,116.46,0.34,0.13
+Cu,3.48,136.99,5.86,11.97,1.07,0.72
+Dy,4.23,,,31.37,1.72,
+Er,1.55,16.02,2.66,40.96,1.02,0.47
+Fe,4.69,283.59,4.89,10.22,2.32,1.38
+Ga,2.61,29.64,6.02,18.91,-0.04,0.18
+Ge,3.40,,,19.51,0.10,
+He,0.01,1.60,3.36,16.54,0.00,0.02
+Hf,6.41,107.35,3.59,22.26,2.09,0.81
+Ho,4.20,40.86,4.04,30.88,1.74,0.73
+In,2.31,33.65,4.53,27.33,0.28,0.23
+Ir,7.23,345.27,5.44,14.53,1.55,2.54
+K,0.86,3.20,6.19,73.60,0.34,0.16
+Kr,0.02,0.80,7.90,57.44,0.00,0.07
+La,4.22,24.83,3.36,37.10,1.44,0.21
+Li,1.61,13.62,3.75,20.22,0.60,0.13
+Mg,1.49,36.05,4.04,23.03,0.82,0.41
+Mn,3.76,276.38,5.42,10.73,2.38,0.65
+Mn_mag,3.76,275.57,5.58,10.72,2.36,0.69
+Na,1.11,9.61,3.08,35.08,0.38,0.14
+Ni,4.77,197.87,5.29,10.85,1.39,0.94
+Ni_mag,4.83,193.06,5.45,10.90,1.43,1.08
+Os,8.17,398.14,4.99,14.29,2.75,2.73
+Pa,6.86,96.13,4.16,25.21,1.54,0.77
+Pb,2.94,39.25,4.07,31.69,0.45,0.54
+Pd,3.71,165.83,6.62,15.37,1.16,0.95
+Pr,3.58,,,23.79,0.76,
+Pt,5.45,246.74,6.10,15.67,0.61,1.24
+Rb,0.76,2.40,7.13,90.81,0.30,0.14
+Re,7.73,366.10,4.73,14.94,2.94,1.81
+Rh,5.63,250.74,5.86,14.13,1.57,1.79
+Ru,6.56,314.03,4.81,13.88,2.51,1.85
+Sc,4.09,54.47,4.07,24.43,2.07,0.60
+Sn,3.11,46.46,6.10,27.82,0.26,0.38
+Sr,1.61,11.22,3.72,54.74,0.95,0.45
+Ta,8.09,198.67,3.86,18.63,2.23,
+Tb,4.26,38.45,4.55,31.88,1.75,0.71
+Tc,6.84,298.81,4.91,14.50,2.62,1.17
+Th,6.34,55.28,3.50,32.07,1.92,1.25
+Ti,5.43,102.54,2.89,17.25,1.95,0.45
+Tl,1.99,27.24,5.55,30.50,0.37,0.10
+W,7.98,,,16.25,1.67,
+Xe,0.03,0.80,2.22,80.21,0.01,0.09
+Y,4.14,38.45,4.10,32.35,1.72,0.67
+Zr,6.21,90.52,4.42,23.26,2.02,0.50

examples/vacancy_migration/Table-A2-hcp.csv

@@ -0,0 +1,58 @@
+symbol,cohesive_energy,K,K',volume_per_atom,e_vacform,e_vacmig_v,e_vacmig
+Ag,2.49,82.51,5.15,17.93,0.76,0.50,0.60
+Al,3.40,74.50,4.70,16.63,0.62,0.41,0.46
+Ar,0.02,0.88,7.43,46.11,0.01,0.06,
+Au,2.98,130.58,7.58,18.10,0.40,0.49,0.57
+Ba,1.87,8.01,3.38,63.55,1.11,0.34,0.37
+Be,3.72,122.57,3.57,7.92,1.04,0.74,0.87
+Bi,2.38,,,31.44,0.00,,
+Ca,1.91,17.62,3.44,42.39,1.06,0.38,0.42
+Cd,0.74,43.26,6.04,22.60,0.25,0.23,0.16
+Ce,4.49,,,26.43,1.23,,
+Co,4.90,248.34,3.29,10.33,1.67,1.02,1.19
+Co_mag,5.12,214.69,4.91,10.85,1.93,0.81,0.78
+Cr,3.63,,,11.77,1.77,,
+Cs,0.70,1.60,7.60,116.42,0.31,0.12,0.13
+Cu,3.47,134.58,5.61,12.00,1.04,0.57,0.69
+Fe,4.77,288.39,5.60,10.16,2.43,1.52,1.51
+Ga,2.60,44.06,4.37,18.92,0.21,0.12,0.16
+Ge,3.40,,,19.25,0.26,,
+He,0.01,1.60,3.46,16.54,0.00,0.02,0.01
+Hf,6.48,112.95,3.59,22.26,2.26,0.89,1.00
+In,2.31,32.84,4.68,27.40,0.31,0.20,0.21
+Ir,7.16,339.66,3.32,14.60,1.25,1.57,1.95
+K,0.86,3.20,5.95,73.72,0.35,0.12,0.14
+Kr,0.02,0.80,6.73,58.10,0.00,0.07,0.07
+La,4.20,,,37.42,1.45,,
+Li,1.61,13.62,3.82,20.24,0.63,0.10,0.12
+Mg,1.50,36.05,4.31,22.85,0.78,0.40,0.42
+Mn,3.82,190.66,11.04,10.63,2.51,0.43,0.11
+Mo,5.91,,,15.95,1.96,,
+Nb,6.71,,,18.61,2.08,,
+Ne,0.01,6.41,1.65,19.15,-0.01,0.04,0.04
+Ni,4.74,134.58,12.07,10.87,1.35,0.69,0.79
+Ni_mag,4.81,192.26,5.34,10.94,1.37,0.77,0.89
+Os,8.31,406.15,5.04,14.23,3.03,3.03,3.22
+Pa,3.09,90.52,3.96,15.12,0.28,0.38,
+Pb,2.92,37.65,4.49,31.51,0.41,0.44,0.41
+Pd,3.67,163.42,6.50,15.45,1.10,0.68,0.73
+Pt,5.39,241.13,6.22,15.77,0.70,0.70,
+Rb,0.76,2.40,6.45,91.19,0.32,0.13,0.12
+Re,7.79,370.10,4.67,14.89,3.42,1.79,1.17
+Rh,5.59,250.74,5.76,14.18,1.53,1.25,1.47
+Ru,6.68,311.62,5.16,13.81,2.68,2.17,2.18
+Sc,4.14,52.87,4.82,24.48,1.87,0.74,0.69
+Si,4.04,85.72,4.88,14.35,0.08,0.04,0.26
+Sn,3.11,47.26,5.92,27.55,0.40,0.31,0.33
+Sr,1.61,11.22,3.77,55.16,0.98,0.36,0.40
+Ta,8.05,,,18.55,2.35,,
+Tc,6.91,302.01,4.90,14.44,2.85,1.21,0.63
+Te,2.23,48.87,5.03,31.47,0.25,,
+Ti,5.49,109.75,5.07,17.29,2.06,0.49,0.59
+Tl,2.01,25.63,5.47,30.97,0.40,0.23,0.16
+V,5.15,,,13.75,1.84,,
+W,7.96,,,16.32,2.44,,
+Xe,0.03,0.80,2.32,79.60,0.01,0.07,0.08
+Y,4.16,40.05,3.72,32.82,1.87,0.69,0.67
+Zn,1.10,73.70,6.02,15.23,0.47,0.34,0.20
+Zr,6.25,93.73,4.34,23.44,2.03,0.57,0.70

examples/vacancy_migration/analysis.py

@@ -0,0 +1,284 @@
+import glob
+import pickle
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+from pymatgen.core import Element
+
+from mlip_arena.models import REGISTRY
+
+DATA_DIR = Path(__file__).parent
+
+mlip_models = ["MACE-MP(M)", "MatterSim", "ORBv2", "M3GNet", "CHGNet", "SevenNet"]
+
+fcc_pbe = pd.read_csv(DATA_DIR / "Table-A1-fcc.csv")
+hcp_pbe = pd.read_csv(DATA_DIR / "Table-A2-hcp.csv")
+
+# fcc
+
+# Initialize an empty DataFrame
+results_df = pd.DataFrame(columns=["symbol", "model", "fit_path", "fit_energies"])
+
+for model in mlip_models:
+    out_dir = Path(REGISTRY[model]["family"])
+
+    for index, row in fcc_pbe.iterrows():
+        symbol = row["symbol"]
+
+        if Element(symbol.split("_")[0]).is_noble_gas:
+            continue
+
+        files = glob.glob(str(out_dir / f"{model}-fcc-{symbol.split('_')[0]}108.pkl"))
+        if len(files) == 0:
+            print("skip", model, symbol)
+            # Add missing data to the DataFrame
+            # if symbol not in results_df['symbol'].values:
+            # Create a new row if the symbol is not yet in the DataFrame
+            new_row = {
+                "symbol": symbol,
+                "model": model,
+                "pbe_e_vacmig": row["e_vacmig"],
+                "fit_path": [],
+                "fit_energies": [],
+            }
+            results_df = pd.concat(
+                [results_df, pd.DataFrame([new_row])], ignore_index=True
+            )
+            continue
+        file = files[0]
+        with open(file, "rb") as f:
+            result = pickle.load(f)
+
+        # Add data to the DataFrame
+        # if symbol not in results_df['symbol'].values:
+        # Create a new row if the symbol is not yet in the DataFrame
+        forcefit = result["neb"]["forcefit"]
+        new_row = {
+            "symbol": symbol,
+            "model": model,
+            "pbe_e_vacmig": row["e_vacmig"],
+            "fit_path": forcefit.fit_path,
+            "fit_energies": forcefit.fit_energies,
+        }
+        results_df = pd.concat([results_df, pd.DataFrame([new_row])], ignore_index=True)
+
+
+nrows = 2
+ncols = len(mlip_models) // nrows
+
+fig, axes = plt.subplots(
+    nrows=nrows,
+    ncols=ncols,
+    figsize=(6, 4),
+    sharex=True,
+    sharey=True,
+    constrained_layout=True,
+    dpi=300,
+)
+
+for i, (ax, model) in enumerate(zip(axes.ravel(), mlip_models, strict=False)):
+    filtered_df = results_df[results_df["model"] == model]
+
+    asymmetries = []
+    middle_deviations = []
+
+    for index, row in filtered_df.iterrows():
+        if len(row["fit_path"]) == 0 or pd.isna(row["pbe_e_vacmig"]):
+            continue
+
+        x = row["fit_path"] / max(row["fit_path"])
+        y = row["fit_energies"] / row["pbe_e_vacmig"]
+
+        # middle_idx = np.argmin(np.abs(x - 0.5))
+
+        left_side = y[x <= 0.5]
+        right_side = y[x >= 0.5][::-1]
+        min_len = min(len(left_side), len(right_side))
+        left_side = left_side[:min_len]
+        right_side = right_side[:min_len]
+
+        asymmetry = np.abs(left_side - right_side).mean()
+        # middle = (left_side[-1] + right_side[-1]) / 2
+        middle = max(y)
+
+        if np.abs(np.array(y)).max() > 10:
+            continue
+
+        asymmetries.append(asymmetry)
+        middle_deviations.append(middle - 1)
+
+        ax.plot(
+            x,
+            y,
+            alpha=0.5,
+            color=method_color_mapping[model],
+            label=model,
+        )
+
+    asymmetries = np.array(asymmetries)
+    middle_deviations = np.array(middle_deviations)
+
+    ax.text(
+        0.05,
+        0.95,
+        "\n".join(
+            [
+                f"Miss: {len(filtered_df) - len(asymmetries) - filtered_df['pbe_e_vacmig'].isna().sum()}",
+                f"Asym: {asymmetries.mean():.3f}",
+                f"MAPE@max: {np.abs(middle_deviations).mean() * 100:.1f}",
+            ]
+        ),
+        transform=ax.transAxes,
+        ha="left",
+        va="top",
+        fontsize="small",
+        # fontsize=6,
+    )
+
+    ax.set(
+        title=model,
+        xlabel="Normalized path" if i >= len(models) - ncols else None,
+        ylabel="Normalized energy" if i % ncols == 0 else None,
+        ylim=(-0.1, 2),
+    )
+
+with open(DATA_DIR / "fcc.pkl", "wb") as f:
+    pickle.dump(fig, f)
+
+# hcp
+
+# Initialize an empty DataFrame
+results_df = pd.DataFrame(columns=["symbol", "model", "fit_path", "fit_energies"])
+
+for model in mlip_models:
+    out_dir = Path(REGISTRY[model]["family"])
+
+    for index, row in hcp_pbe.iterrows():
+        symbol = row["symbol"]
+
+        if Element(symbol.split("_")[0]).is_noble_gas:
+            continue
+
+        files = glob.glob(str(out_dir / f"{model}-hcp-{symbol.split('_')[0]}36.pkl"))
+        if len(files) == 0:
+            print("skip", model, symbol)
+            # Add missing data to the DataFrame
+            # if symbol not in results_df['symbol'].values:
+            # Create a new row if the symbol is not yet in the DataFrame
+            new_row = {
+                "symbol": symbol,
+                "model": model,
+                "pbe_e_vacmig": row["e_vacmig"],
+                "fit_path": [],
+                "fit_energies": [],
+            }
+            results_df = pd.concat(
+                [results_df, pd.DataFrame([new_row])], ignore_index=True
+            )
+            # else:
+            #     # Update the existing row with the model's prediction
+            #     results_df.loc[results_df['symbol'] == symbol, model] = pd.NA
+            continue
+        file = files[0]
+        with open(file, "rb") as f:
+            result = pickle.load(f)
+
+        # Add data to the DataFrame
+        # if symbol not in results_df['symbol'].values:
+        # Create a new row if the symbol is not yet in the DataFrame
+        forcefit = result["neb"]["forcefit"]
+        new_row = {
+            "symbol": symbol,
+            "model": model,
+            "pbe_e_vacmig": row["e_vacmig"],
+            "fit_path": forcefit.fit_path,
+            "fit_energies": forcefit.fit_energies,
+        }
+        results_df = pd.concat([results_df, pd.DataFrame([new_row])], ignore_index=True)
+
+
+
+nrows = 2
+ncols = len(mlip_models) // nrows
+
+threshold = 0.10
+
+fig, axes = plt.subplots(
+    nrows=nrows,
+    ncols=ncols,
+    figsize=(6, 4),
+    sharex=True,
+    sharey=True,
+    constrained_layout=True,
+    dpi=300,
+)
+
+for i, (ax, model) in enumerate(zip(axes.ravel(), mlip_models, strict=False)):
+    filtered_df = results_df[results_df["model"] == model]
+
+    asymmetries = []
+    middle_deviations = []
+
+    for index, row in filtered_df.iterrows():
+        if len(row["fit_path"]) == 0 or pd.isna(row["pbe_e_vacmig"]):
+            continue
+
+        x = row["fit_path"] / max(row["fit_path"])
+        y = row["fit_energies"] / row["pbe_e_vacmig"]
+
+        # middle_idx = np.argmin(np.abs(x - 0.5))
+
+        left_side = y[x <= 0.5]
+        right_side = y[x >= 0.5][::-1]
+        min_len = min(len(left_side), len(right_side))
+        left_side = left_side[:min_len]
+        right_side = right_side[:min_len]
+
+        asymmetry = np.abs(left_side - right_side).mean()
+        # middle = (left_side[-1] + right_side[-1]) / 2
+        middle = max(y)
+
+        if np.abs(np.array(y)).max() > 10:
+            continue
+
+        asymmetries.append(asymmetry)
+        middle_deviations.append(middle - 1)
+
+        ax.plot(
+            x,
+            y,
+            alpha=0.5,
+            color=method_color_mapping[model],
+            label=model,
+        )
+
+    asymmetries = np.array(asymmetries)
+    middle_deviations = np.array(middle_deviations)
+
+    ax.text(
+        0.05,
+        0.95,
+        "\n".join(
+            [
+                f"Miss: {len(filtered_df) - len(asymmetries) - filtered_df['pbe_e_vacmig'].isna().sum()}",
+                f"Asym: {asymmetries.mean():.3f}",
+                f"MAPE@max: {np.abs(middle_deviations).mean() * 100:.1f}",
+            ]
+        ),
+        transform=ax.transAxes,
+        ha="left",
+        va="top",
+        fontsize="small",
+    )
+
+    ax.set(
+        title=model,
+        xlabel="Normalized path" if i >= len(mlip_models) - ncols else None,
+        ylabel="Normalized energy" if i % ncols == 0 else None,
+        ylim=(-0.1, 2),
+    )
+
+with open(DATA_DIR / "hcp.pkl", "wb") as f:
+    pickle.dump(fig, f)

examples/wbm_ev/analyze.py

@@ -0,0 +1,210 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from ase.db import connect
+from scipy import stats
+
+from mlip_arena.models import REGISTRY, MLIPEnum
+
+DATA_DIR = Path(__file__).parent.absolute()
+
+
+def load_wbm_structures():
+    """
+    Load the WBM structures from a ASE DB file.
+    """
+    with connect(DATA_DIR.parent / "wbm_structures.db") as db:
+        for row in db.select():
+            yield row.toatoms(add_additional_information=True)
+
+def gather_results():
+    for model in MLIPEnum:
+        if "wbm_ev" not in REGISTRY[model.name].get("gpu-tasks", []):
+            continue
+
+        if (DATA_DIR / f"{model.name}.parquet").exists():
+            continue
+
+        all_data = []
+
+        for atoms in load_wbm_structures():
+            fpath = Path(model.name) / f"{atoms.info['key_value_pairs']['wbm_id']}.json"
+            if not fpath.exists():
+                continue
+
+            all_data.append(pd.read_json(fpath))
+
+        df = pd.concat(all_data, ignore_index=True)
+        df.to_parquet(DATA_DIR / f"{model.name}.parquet")
+
+
+def summarize():
+    summary_table = pd.DataFrame(
+        columns=[
+            "model",
+            "energy-diff-flip-times",
+            "tortuosity",
+            "spearman-compression-energy",
+            "spearman-compression-derivative",
+            "spearman-tension-energy",
+            "missing",
+        ]
+    )
+
+
+    for model in MLIPEnum:
+        fpath = DATA_DIR / f"{model.name}.parquet"
+        if not fpath.exists():
+            continue
+        df_raw_results = pd.read_parquet(fpath)
+
+        df_analyzed = pd.DataFrame(
+            columns=[
+                "model",
+                "structure",
+                "formula",
+                "volume-ratio",
+                "energy-delta-per-atom",
+                "energy-diff-flip-times",
+                "tortuosity",
+                "spearman-compression-energy",
+                "spearman-compression-derivative",
+                "spearman-tension-energy",
+                "missing",
+            ]
+        )
+
+        for wbm_struct in load_wbm_structures():
+            structure_id = wbm_struct.info["key_value_pairs"]["wbm_id"]
+
+            try:
+                results = df_raw_results.loc[df_raw_results["id"] == structure_id]
+                results = results["eos"].values[0]
+                es = np.array(results["energies"])
+                vols = np.array(results["volumes"])
+                vol0 = wbm_struct.get_volume()
+
+                indices = np.argsort(vols)
+                vols = vols[indices]
+                es = es[indices]
+
+                imine = len(es) // 2
+                # min_center_val = np.min(es[imid - 1 : imid + 2])
+                # imine = np.where(es == min_center_val)[0][0]
+                emin = es[imine]
+
+                interpolated_volumes = [
+                    (vols[i] + vols[i + 1]) / 2 for i in range(len(vols) - 1)
+                ]
+                ediff = np.diff(es)
+                ediff_sign = np.sign(ediff)
+                mask = ediff_sign != 0
+                ediff = ediff[mask]
+                ediff_sign = ediff_sign[mask]
+                ediff_flip = np.diff(ediff_sign) != 0
+
+                etv = np.sum(np.abs(np.diff(es)))
+
+                data = {
+                    "model": model.name,
+                    "structure": structure_id,
+                    "formula": wbm_struct.get_chemical_formula(),
+                    "missing": False,
+                    "volume-ratio": vols / vol0,
+                    "energy-delta-per-atom": (es - emin) / len(wbm_struct),
+                    "energy-diff-flip-times": np.sum(ediff_flip).astype(int),
+                    "tortuosity": etv / (abs(es[0] - emin) + abs(es[-1] - emin)),
+                    "spearman-compression-energy": stats.spearmanr(
+                        vols[:imine], es[:imine]
+                    ).statistic,
+                    "spearman-compression-derivative": stats.spearmanr(
+                        interpolated_volumes[:imine], ediff[:imine]
+                    ).statistic,
+                    "spearman-tension-energy": stats.spearmanr(
+                        vols[imine:], es[imine:]
+                    ).statistic,
+                }
+
+            except Exception:
+                data = {
+                    "model": model.name,
+                    "structure": structure_id,
+                    "formula": wbm_struct.get_chemical_formula(),
+                    "missing": True,
+                    "volume-ratio": None,
+                    "energy-delta-per-atom": None,
+                    "energy-diff-flip-times": None,
+                    "tortuosity": None,
+                    "spearman-compression-energy": None,
+                    "spearman-compression-derivative": None,
+                    "spearman-tension-energy": None,
+                }
+
+            df_analyzed = pd.concat([df_analyzed, pd.DataFrame([data])], ignore_index=True)
+
+        df_analyzed.to_parquet(DATA_DIR / f"{model.name}_processed.parquet")
+        # json_fpath = DATA_DIR / f"EV_scan_analyzed_{model.name}.json"
+
+        # df_analyzed.to_json(json_fpath, orient="records")
+
+        valid_results = df_analyzed[df_analyzed["missing"] == False]
+
+        analysis_summary = {
+            "model": model.name,
+            "energy-diff-flip-times": valid_results["energy-diff-flip-times"].mean(),
+            "tortuosity": valid_results["tortuosity"].mean(),
+            "spearman-compression-energy": valid_results[
+                "spearman-compression-energy"
+            ].mean(),
+            "spearman-compression-derivative": valid_results[
+                "spearman-compression-derivative"
+            ].mean(),
+            "spearman-tension-energy": valid_results["spearman-tension-energy"].mean(),
+            "missing": len(df_analyzed[df_analyzed["missing"] == True]),
+        }
+        summary_table = pd.concat(
+            [summary_table, pd.DataFrame([analysis_summary])], ignore_index=True
+        )
+
+
+    flip_rank = (
+        (summary_table["energy-diff-flip-times"] - 1)
+        .abs()
+        .rank(ascending=True, method="min")
+    )
+    tortuosity_rank = summary_table["tortuosity"].rank(ascending=True, method="min")
+    spearman_compression_energy_rank = summary_table["spearman-compression-energy"].rank(
+        method="min"
+    )
+    spearman_compression_derivative_rank = summary_table[
+        "spearman-compression-derivative"
+    ].rank(ascending=False, method="min")
+    spearman_tension_energy_rank = summary_table["spearman-tension-energy"].rank(
+        ascending=False, method="min"
+    )
+    missing_rank = summary_table["missing"].rank(ascending=True, method="min")
+
+    rank_aggr = (
+        flip_rank
+        + tortuosity_rank
+        + spearman_compression_energy_rank
+        + spearman_compression_derivative_rank
+        + spearman_tension_energy_rank
+        + missing_rank
+    )
+    rank = rank_aggr.rank(method="min")
+
+    summary_table.insert(1, "rank", rank.astype(int))
+    summary_table.insert(2, "rank-aggregation", rank_aggr.astype(int))
+    summary_table = summary_table.sort_values(by="rank", ascending=True)
+    summary_table = summary_table.reset_index(drop=True)
+
+    summary_table.to_csv(DATA_DIR / "summary.csv", index=False)
+    summary_table.to_latex(DATA_DIR / "summary.tex", index=False)
+
+    return summary_table
+
+if __name__ == "__main__":
+    gather_results()
+    summarize()

mlip_arena/models/externals/fairchem.py

@@ -87,7 +87,7 @@ class EquiformerV2(OCPCalculator):
         self,
         checkpoint=REGISTRY["EquiformerV2(OC22)"]["checkpoint"],
         # TODO: cannot assign device
-        local_cache="/tmp/ocp/",
+        local_cache="~/.cache/ocp/",
         cpu=False,
         seed=0,
         **kwargs,
@@ -114,7 +114,7 @@ class EquiformerV2OC20(OCPCalculator):
         self,
         checkpoint=REGISTRY["EquiformerV2(OC22)"]["checkpoint"],
         # TODO: cannot assign device
-        local_cache="/tmp/ocp/",
+        local_cache="~/.cache/ocp/",
         cpu=False,
         seed=0,
         **kwargs,
@@ -133,7 +133,7 @@ class eSCN(OCPCalculator):
         self,
         checkpoint="eSCN-L6-M3-Lay20-S2EF-OC20-All+MD",  # TODO: import from registry
         # TODO: cannot assign device
-        local_cache="/tmp/ocp/",
+        local_cache="~/.cache/ocp/",
         cpu=False,
         seed=0,
         **kwargs,

mlip_arena/tasks/diatomics/homonuclear-diatomics.tex

@@ -0,0 +1,23 @@
+\begin{tabular}{lrrrrrrrrrrr}
+\toprule
+ & Rank & Rank aggr. & Conservation deviation [eV/Å] & Spearman's coeff. (E: repulsion) & Spearman's coeff. (F: descending) & Energy jump [eV] & Force flips & Tortuosity \\
+Model &  &  &  &  &  &  &  &  \\
+\midrule
+MACE-MPA & 1 & 13 & 0.077 & -0.997 & -0.975 & 0.010 & 1.371 & 1.006 \\
+MACE-MP(M) & 2 & 15 & 0.070 & -0.997 & -0.980 & 0.038 & 1.449 & 1.161 \\
+MatterSim & 3 & 20 & 0.013 & -0.980 & -0.972 & 0.008 & 2.766 & 1.021 \\
+M3GNet & 4 & 24 & 0.026 & -0.991 & -0.947 & 0.029 & 3.528 & 1.016 \\
+ORBv2 & 5 & 30 & 9.751 & -0.883 & -0.988 & 0.991 & 0.991 & 1.287 \\
+eSCN(OC20) & 6 & 32 & 2.045 & -0.939 & -0.984 & 0.806 & 0.640 & 5.335 \\
+CHGNet & 7 & 35 & 1.066 & -0.992 & -0.925 & 0.291 & 2.255 & 2.279 \\
+ORB & 8 & 39 & 10.220 & -0.881 & -0.954 & 1.019 & 1.026 & 1.798 \\
+SevenNet & 9 & 41 & 34.005 & -0.986 & -0.928 & 0.392 & 2.112 & 1.292 \\
+eqV2(OMat) & 10 & 51 & 15.477 & -0.880 & -0.976 & 4.118 & 3.126 & 2.515 \\
+eSEN & 11 & 55 & 1.170 & -0.692 & -0.919 & 5.562 & 4.000 & 1.838 \\
+ALIGNN & 12 & 59 & 5.164 & -0.913 & -0.310 & 9.876 & 30.669 & 1.818 \\
+EquiformerV2(OC20) & 13 & 71 & 21.385 & -0.680 & -0.891 & 38.282 & 22.775 & 8.669 \\
+EquiformerV2(OC22) & 14 & 75 & 27.687 & -0.415 & -0.855 & 64.837 & 21.674 & 15.880 \\
+MACE-OFF(M) & 15 & 85 & NaN & NaN & NaN & NaN & NaN & NaN \\
+ANI2x & 16 & 91 & NaN & NaN & NaN & NaN & NaN & NaN \\
+\bottomrule
+\end{tabular}

mlip_arena/tasks/registry.yaml

@@ -31,3 +31,8 @@ Lattice thermal conductivity:
   task-page: thermal-conductivity
   task-layout: centered
   rank-page: thermal-conductivity
+# 2D materials:
+#   category: Properties and Physical Behaviors
+#   task-page: c2db
+#   task-layout: centered
+#   rank-page: 

serve/ranks/homonuclear-diatomics.py

@@ -20,6 +20,24 @@ dfs = [
 ]
 df = pd.concat(dfs, ignore_index=True)
 
+# df = df[df["method"].isin([
+#     "SevenNet",
+#     "ORBv2",
+#     "ORB",
+#     "MatterSim",
+#     "MACE-MPA",
+#     "MACE-MP(M)",
+#     "M3GNet",
+#     "eSEN",
+#     "eSCN(OC20)",
+#     "eqV2(OMat)",
+#     "EquiformerV2(OC22)",
+#     "EquiformerV2(OC20)",
+#     "CHGNet",
+#     "ALIGNN"
+# ]
+# )]
+
 table = pd.DataFrame()
 
 for model in valid_models:
@@ -29,21 +47,13 @@ for model in valid_models:
     new_row = {
         "Model": model,
         "Conservation deviation [eV/Å]": rows["conservation-deviation"].mean(),
-        "Spearman's coeff. (E: repulsion)": rows[
-            "spearman-repulsion-energy"
-        ].mean(),
-        "Spearman's coeff. (F: descending)": rows[
-            "spearman-descending-force"
-        ].mean(),
+        "Spearman's coeff. (E: repulsion)": rows["spearman-repulsion-energy"].mean(),
+        "Spearman's coeff. (F: descending)": rows["spearman-descending-force"].mean(),
         "Tortuosity": rows["tortuosity"].mean(),
         "Energy jump [eV]": rows["energy-jump"].mean(),
         "Force flips": rows["force-flip-times"].mean(),
-        "Spearman's coeff. (E: attraction)": rows[
-            "spearman-attraction-energy"
-        ].mean(),
-        "Spearman's coeff. (F: ascending)": rows[
-            "spearman-ascending-force"
-        ].mean(),
+        "Spearman's coeff. (E: attraction)": rows["spearman-attraction-energy"].mean(),
+        "Spearman's coeff. (F: ascending)": rows["spearman-ascending-force"].mean(),
         "PBE energy MAE [eV]": rows["pbe-energy-mae"].mean(),
         "PBE force MAE [eV/Å]": rows["pbe-force-mae"].mean(),
     }
@@ -55,14 +65,10 @@ table.set_index("Model", inplace=True)
 table.sort_values("Conservation deviation [eV/Å]", ascending=True, inplace=True)
 table["Rank"] = np.argsort(table["Conservation deviation [eV/Å]"].to_numpy())
 
-table.sort_values(
-    "Spearman's coeff. (E: repulsion)", ascending=True, inplace=True
-)
+table.sort_values("Spearman's coeff. (E: repulsion)", ascending=True, inplace=True)
 table["Rank"] += np.argsort(table["Spearman's coeff. (E: repulsion)"].to_numpy())
 
-table.sort_values(
-    "Spearman's coeff. (F: descending)", ascending=True, inplace=True
-)
+table.sort_values("Spearman's coeff. (F: descending)", ascending=True, inplace=True)
 table["Rank"] += np.argsort(table["Spearman's coeff. (F: descending)"].to_numpy())
 
 # NOTE: it's not fair to models trained on different level of theory
@@ -83,28 +89,45 @@ table["Rank"] += np.argsort(np.abs(table["Force flips"].to_numpy() - 1))
 
 table["Rank"] += 1
 
-table.sort_values(["Rank", "Conservation deviation [eV/Å]"], ascending=True, inplace=True)
+table.sort_values(
+    ["Rank", "Conservation deviation [eV/Å]"], ascending=True, inplace=True
+)
 
 table["Rank aggr."] = table["Rank"]
-table["Rank"] = table["Rank aggr."].rank(method='min').astype(int)
+table["Rank"] = table["Rank aggr."].rank(method="min").astype(int)
 
 table = table.reindex(
     columns=[
         "Rank",
         "Rank aggr.",
         "Conservation deviation [eV/Å]",
-        "PBE energy MAE [eV]",
-        "PBE force MAE [eV/Å]",
         "Spearman's coeff. (E: repulsion)",
         "Spearman's coeff. (F: descending)",
         "Energy jump [eV]",
         "Force flips",
         "Tortuosity",
+        "PBE energy MAE [eV]",
+        "PBE force MAE [eV/Å]",
         "Spearman's coeff. (E: attraction)",
         "Spearman's coeff. (F: ascending)",
     ]
 )
 
+# cloned = table.copy()
+# cloned.drop(columns=[
+#     "PBE energy MAE [eV]",
+#     "PBE force MAE [eV/Å]",
+#     "Spearman's coeff. (E: attraction)",
+#     "Spearman's coeff. (F: ascending)",],
+#     inplace=True
+# )
+# cloned.to_latex(
+#     DATA_DIR / "homonuclear-diatomics.tex",
+#     float_format="%.3f",
+#     index=True,
+#     column_format="l" + "r" * (len(table.columns) - 1),
+# )
+
 s = (
     table.style.background_gradient(
         cmap="viridis_r",
@@ -135,7 +158,7 @@ s = (
         subset=["Rank", "Rank aggr."],
     )
     .format(
-        "{:.3f}", 
+        "{:.3f}",
         subset=[
             "Conservation deviation [eV/Å]",
             "Spearman's coeff. (E: repulsion)",
@@ -147,7 +170,7 @@ s = (
             "Spearman's coeff. (F: ascending)",
             "PBE energy MAE [eV]",
             "PBE force MAE [eV/Å]",
-        ]
+        ],
     )
 )
 
@@ -177,4 +200,7 @@ def render():
             - **Force flips**: The number of force direction changes.
             """
         )
-        st.info('PBE energies and forces are provided __only__ for reference. Due to the known convergence issue of plane-wave DFT with diatomic molecules and different dataset the models might be trained on, comparing models with PBE is not rigorous and thus these metrics are excluded from rank aggregation.', icon=":material/warning:")
+        st.info(
+            "PBE energies and forces are provided __only__ for reference. Due to the known convergence issue of plane-wave DFT with diatomic molecules and different dataset the models might be trained on, comparing models with PBE is not rigorous and thus these metrics are excluded from rank aggregation.",
+            icon=":material/warning:",
+        )

serve/tasks/eos_bulk.py

@@ -34,7 +34,7 @@ selected_models = methods_container.multiselect(
 )
 
 # Visualization settings
-st.markdown("### Visualization Settings")
+st.markdown("### Visualization settings")
 vis = st.container(border=True)
 
 # Column settings
@@ -83,6 +83,7 @@ def generate_dataframe(model_name):
             "formula",
             "volume-ratio",
             "energy-delta-per-atom",
+            "energy-delta-per-volume-b0",
             "energy-diff-flip-times",
             "tortuosity",
             "spearman-compression-energy",
@@ -97,10 +98,12 @@ def generate_dataframe(model_name):
 
         try:
             results = df_raw_results.loc[df_raw_results["id"] == structure_id]
+            b0 = results["b0"].values[0]
+            # vol0 = results["v0"].values[0]
             results = results["eos"].values[0]
             es = np.array(results["energies"])
             vols = np.array(results["volumes"])
-            vol0 = wbm_struct.get_volume()
+            
 
             indices = np.argsort(vols)
             vols = vols[indices]
@@ -110,6 +113,7 @@ def generate_dataframe(model_name):
             # min_center_val = np.min(es[imid - 1 : imid + 2])
             # imine = np.where(es == min_center_val)[0][0]
             emin = es[imine]
+            vol0 = vols[imine]
 
             interpolated_volumes = [
                 (vols[i] + vols[i + 1]) / 2 for i in range(len(vols) - 1)
@@ -131,6 +135,7 @@ def generate_dataframe(model_name):
                 "volume-ratio": vols / vol0,
                 "energy-delta-per-atom": (es - emin) / len(wbm_struct),
                 "energy-diff-flip-times": np.sum(ediff_flip).astype(int),
+                "energy-delta-per-volume-b0": (es - emin) / (vol0 * b0),
                 "tortuosity": etv / (abs(es[0] - emin) + abs(es[-1] - emin)),
                 "spearman-compression-energy": stats.spearmanr(
                     vols[:imine], es[:imine]
@@ -151,6 +156,7 @@ def generate_dataframe(model_name):
                 "missing": True,
                 "volume-ratio": None,
                 "energy-delta-per-atom": None,
+                "energy-delta-per-volume-b0": None,
                 "energy-diff-flip-times": None,
                 "tortuosity": None,
                 "spearman-compression-energy": None,
@@ -185,10 +191,10 @@ def get_plots(selected_models):
             structure_id = row["structure"]
             formula = row.get("formula", "")
             if isinstance(row["volume-ratio"], list | np.ndarray) and isinstance(
-                row["energy-delta-per-atom"], list | np.ndarray
+                row["energy-delta-per-volume-b0"], list | np.ndarray
             ):
                 vol_strain = row["volume-ratio"]
-                energy_delta = row["energy-delta-per-atom"]
+                energy_delta = row["energy-delta-per-volume-b0"]
                 color = color_sequence[i % len(color_sequence)]
                 fig.add_trace(
                     go.Scatter(
@@ -203,7 +209,7 @@ def get_plots(selected_models):
                             structure_id + "<br>"
                             "Formula: " + str(formula) + "<br>"
                             "Volume ratio V/V₀: %{x:.3f}<br>"
-                            "ΔEnergy: %{y:.3f} eV/atom<br>"
+                            "ΔE/(BV₀): %{y:.3f} eV/atom<br>"
                             "<extra></extra>"
                         ),
 
@@ -215,10 +221,10 @@ def get_plots(selected_models):
         fig.update_layout(
             title=f"{model_name} ({len(valid_structures)} / {len(df)} structures)",
             xaxis_title="Volume ratio V/V₀",
-            yaxis_title="Relative energy ΔE (eV/atom)",
+            yaxis_title="Relative energy ΔE/(BV₀)",
             height=500,
             showlegend=False,  # Disable legend for the whole plot
-            yaxis=dict(range=[-0.1, 1]),  # Set y-axis limits
+            yaxis=dict(range=[-0.02, 0.1]),  # Set y-axis limits
         )
         fig.add_vline(x=1, line_dash="dash", line_color="gray", opacity=0.7)
         figs.append((model_name, fig, valid_structures))