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Recent advances and applications of deep learning methods in materials science

npj Computational Materials 2022年第1期8卷 548-573页

作者： Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza ryan cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA Theiss Research La JollaCA92037USA DeepMaterials LLC Silver SpringMD20906USA Material Measurement Science Division National Institute of Standards and TechnologyGaithersburgMD20899USA Department of NanoEngineering University of California San DiegoSan DiegoCA92093USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Materials Science and Engineering Carnegie Mellon UniversityPittsburghPA15213USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL60208USA Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied SciencesColumbia UniversityNew YorkNY10027USA

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 *** allows analysis of unstructured data and automated identification of *** recent development of large materials databases has fueled the application of DL methods in atomistic prediction 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 *** this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language *** each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and *** 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 in materials science.

关键词： learning limitations textual

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