Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

• Published in: Chemistry of materials Vol. 32; no. 12; pp. 4954 - 4965
• Main Authors: Wang, Anthony Yu-Tung, Murdock, Ryan J, Kauwe, Steven K, Oliynyk, Anton O, Gurlo, Aleksander, Brgoch, Jakoah, Persson, Kristin A, Sparks, Taylor D
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 23.06.2020; American Chemical Society (ACS)
• Subjects: machine learning; material properties; materials; MATERIALS SCIENCE; neural networks
• ISSN: 0897-4756; 1520-5002
• DOI: 10.1021/acs.chemmater.0c01907

This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.