README.md

---
tags:
- xgboost
- python
- tabular-classification
- chemistry
- pair-distribution-function
model_file: MLstructureMining_model.bin
metrics:
- accuracy
pipeline_tag: tabular-classification
license: apache-2.0
language:
- en
---

# Model description
MLStructureMining is a tree-based machine learning classifier designed to rapidly match X-ray pair distribution function (PDF) data to prototype 
patterns from a large database of crystal structures, providing real-time structure characterization by screening vast quantities of data in seconds.

The code used to train the model can be found [HERE](https://github.com/EmilSkaaning/MLstructureMining-workflow), and the Python implementation can be found 
[HERE](https://github.com/EmilSkaaning/MLstructureMining/tree/main) or the wheel file ´mlstructuremining-4.1.0-py3-none-any.whl´.

## Evaluation Results

MLstructureMining has been trained PDFs from on 10,833 crystal structures obtained from [Crystallography Open Database](https://www.crystallography.net/cod/) (COD). 
The Pearson Correlation Coefficient (PCC) was used to cluster structures with similar PDF, resulting in a total of 6,062 labels. 100 PDFs were simluated per structures,
and the data were split into training, validation and testing set with a 80/10/10 ratio.

We evaluate this model based an accuracy and to test the robustness of MLstructureMining, we deploy zeroth-order optimization (ZOO) 
from the [Adversarial Robustness Toolbox](https://github.com/Trusted-AI/adversarial-robustness-toolbox) (ART) library to perform adversarial attacks.

| Metric   | Value   |
|----------|---------|
| Accuracy | 91% |
| Top-3 Accuracy | 99% |
| ZOO Accuracy | 89% |
| ZOO Top-3 Accuracy | 97% |

# How to Get Started with the Model

Use the code below to get started with the model.

```python
import xgboost as xgb
import pandas as pd

def show_best(pred: np.ndarray, 
              best_list: np.ndarray, 
              df_stru_catalog: pd.DataFrame, 
              num_show: int) -> None:
    """
    Display the best predictions based on the model output.

    Parameters
    ----------
    pred : np.ndarray
        Predictions from the model.
    best_list : np.ndarray
        List of best predictions.
    df_stru_catalog : pd.DataFrame
        The structure catalog associated with the model.
    num_show : int
        Number of top predictions to show.

    Returns
    -------
    None
    """
    for count, idx in enumerate(reversed(best_list[-num_show:])):
        print(f"\n{count}) Probability: {pred[idx]*100:3.1f}%")

        compo = clean_string(df_stru_catalog.iloc[idx]["composition"])
        sgs = clean_string(df_stru_catalog.iloc[idx]["space_group_symmetry"])

        print(f'    COD-IDs: {df_stru_catalog.iloc[idx]["Label"].rsplit(".",1)[0]}, composition: {compo[0]}, space group: {sgs[0]}')
        if not pd.isna(df_stru_catalog.at[idx, "Similar"]):
            similar_files = extract_filenames(df_stru_catalog.at[idx, "Similar"])
            compo = clean_string(df_stru_catalog.iloc[idx]["composition"])
            sgs = clean_string(df_stru_catalog.iloc[idx]["space_group_symmetry"])
            for jdx in range(len(similar_files)):
                print(f'    COD-IDs: {similar_files[jdx]}, composition: {compo[jdx]}, space group: {sgs[jdx]}')


N_CPU = 8  # Number of CPUs used
NUM_SHOW = 5  # Show to X best predictions 

# Load model
bst = xgb.Booster({'nthread': N_CPU})
bst.load_model("MLstructureMining_model.bin")

# Load your data
# data = pd.read_csv("your_data.csv")
# data_xgb = xgb.DMatrix(data)

# Load labels
labels = pd.read_csv("labels.csv", index_col=0)

# Do inference
pred = bst.predict(data_xgb)

# Show 
best_list = np.argsort(pred)
show_best(pred[0], best_list[0], df_stru_catalog, NUM_SHOW)
```


# Model Card Authors

Emil T. S. Kjær

# Model Card Contact

[email protected]

# Citation

In review.

**BibTeX:**
```
@Article{D4DD00001C,
author ="Kjær, Emil T. S. and Anker, Andy S. and Kirsch, Andrea and Lajer, Joakim and Aalling-Frederiksen, Olivia and Billinge, Simon J. L. and Jensen, Kirsten M. Ø.",
title  ="MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions",
journal  ="Digital Discovery",
year  ="2024",
pages  ="-",
publisher  ="RSC",
doi  ="10.1039/D4DD00001C",
url  ="http://dx.doi.org/10.1039/D4DD00001C",
abstract  ="Synchrotron X-ray techniques are essential for studies of the intrinsic relationship between synthesis{,} structure{,} and properties of materials. Modern synchrotrons can produce up to 1 petabyte of data per day. Such amounts of data can speed up materials development{,} but also comes with a staggering growth in workload{,} as the data generated must be stored and analyzed. We present an approach for quickly identifying an atomic structure model from pair distribution function (PDF) data from (nano)crystalline materials. Our model{,} MLstructureMining{,} uses a tree-based machine learning (ML) classifier. MLstructureMining has been trained to classify chemical structures from a PDF and gives a top-3 accuracy of 99% on simulated PDFs not seen during training{,} with a total of 6062 possible classes. We also demonstrate that MLstructureMining can identify the chemical structure from experimental PDFs from nanoparticles of CoFe2O4 and CeO2{,} and we show how it can be used to treat an in situ PDF series collected during Bi2Fe4O9 formation. Additionally{,} we show how MLstructureMining can be used in combination with the well-known methods{,} principal component analysis (PCA) and non-negative matrix factorization (NMF) to analyze data from in situ experiments. MLstructureMining thus allows for real-time structure characterization by screening vast quantities of crystallographic information files in seconds."}
```