Featurization¶

We’ll begin our discussion of featurization with these slides:

References¶

Some helpful references on these topics include [1,2,3,4,5].

1: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning. Volume 103 of Springer Texts in Statistics. Springer, New York, NY, 1st edition, 2017. ISBN 978-1-4614-7137-0.
2: Logan Ward, Ankit Agrawal, Alok Choudhary, and Christopher Wolverton. A general-purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials, 2:16028, 2016. doi:10.1038/npjcompumats.2016.28.
3: Logan Ward, Alexander Dunn, Alireza Faghaninia, Nils E. R. Zimmermann, Saurabh Bajaj, Qi Wang, Joseph Montoya, Jiming Chen, Kyle Bystrom, Maxwell Dylla, Kyle Chard, Mark Asta, Kristin A. Persson, G. Jeffrey Snyder, Ian Foster, and Anubhav Jain. Matminer: An open source toolkit for materials data mining. Computational Materials Science, 152:60–69, 2018. doi:10.1016/j.commatsci.2018.05.018.
4: Lauri Himanen, Marc O. J. Jäger, Eiaki V. Morooka, Filippo Federici Canova, Yashasvi S. Ranawat, David Z. Gao, Patrick Rinke, and Adam S. Foster. DScribe: Library of descriptors for machine learning in materials science. Computer Physics Communications, 247:106949, 2020. doi:10.1016/j.cpc.2019.106949.
5: Greg Landrum. RDKit. GitHub, 2006. URL: https://github.com/rdkit/rdkit.