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

submitted on 05.05.2020, 17:00 and posted on 06.05.2020, 10:18 by Anthony Wang, Ryan Murdock, Steven Kauwe, Anton Oliynyk, Aleksander Gurlo, Jakoah Brgoch, Kristin Persson, Taylor Sparks
This Editorial 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 datasets, 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.


German Academic Exchange Service (program no. 57438025)

NSF CMMI-1562226

NSF DMR-1651668

DOE DE-AC07-05ID145142

NSF DMR 18-47701

NSF CER 19-11311

Welch Foundation E-1981

DOE DE-AC02-05CH11231 KC23MP


Email Address of Submitting Author


Technische Universität Berlin, Chair of Advanced Ceramic Materials



ORCID For Submitting Author


Declaration of Conflict of Interest

The authors declare no conflict of interest.