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Recent Advances and Applications of Deep Learning Methods in Materials Science

Published

Author(s)

Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol WooPark, Alok Choudhary, Ankit Agrawal, Simon Billinge, Elizabeth Holm, ShyuePing Ong, Chris Wolverton

Abstract

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. Deep learning allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we first present a high-level overview of deep-learning methods, followed by a detailed discussion of recent developments of deep learning systems in atomistic simulation, materials image and spectral data analysis, and natural language processing. For each modality, we discuss applications involving both theoretical and experimental data, typical modeling approaches and their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods specially in materials science. The application of DL methods in materials science presents an exciting avenue for futuristic materials discovery and design.
Citation
npj Computational Materials

Keywords

Deep learning, materials science, matrials data

Citation

Choudhary, K. , DeCost, B. , Chen, C. , Jain, A. , Tavazza, F. , Cohn, R. , WooPark, C. , Choudhary, A. , Agrawal, A. , Billinge, S. , Holm, E. , Ong, S. and Wolverton, C. (2022), Recent Advances and Applications of Deep Learning Methods in Materials Science, npj Computational Materials, [online], https://doi.org/10.1038/s41524-022-00734-6, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933541 (Accessed July 23, 2024)

Issues

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Created February 24, 2022, Updated November 29, 2022