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Recent advances and applications of machine learning in solid-state materials science

Authors: J. Schmidt, M.R.G. Marques, S. Botti, and M.A.L. Marques

Ref.: NPJ Comput. Mater. (also appeared in PsiK newsletter, Scientific Highlight of the Month, March) 5, 83 (2019)

Abstract: One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. These are a collection of efficient statistical tools, which have already proved to be capable of speeding up considerably both fundamental and applied research. At present we are witnessing an explosion of works that develop and apply ma-chine learning to solid-state systems. In this article, we provide a comprehensive overview and discussion of the most recent research in this topic. As a starting point we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the different machine learning approaches for the discovery of new stable materials and the prediction of their crystal structure. Then we discuss research into numerous quantitative structure-property relationships and different approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and examples of machine learning applications. Two major questions are always the interpretability of, and the physical understanding gained from, machine learning models. We consider therefore the various facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

Citations: 685 (Google scholar)

DOI: 10.1038/s41524-019-0221-0

URL: psi-k.net



	doi = {10.1038/s41524-019-0221-0},
	url = {https://doi.org/10.1038%2Fs41524-019-0221-0},
	year = 2019,
	month = {aug},
	publisher = {Springer Science and Business Media {LLC}},
	volume = {5},
	number = {1},
	author = {Jonathan Schmidt and M{\'{a}}rio R. G. Marques and Silvana Botti and Miguel A. L. Marques},
	title = {Recent advances and applications of machine learning in solid-state materials science},
	journal = {npj Computational Materials}