Update README.md

README.md

@@ -119,5 +119,14 @@ In review.
 
 **BibTeX:**
 ```
-[More Information Needed]
+@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."}
 ```