Machine learning and artificial neural network accelerated computational discoveries in materials science

Hong, Yang, Hou, Bo ORCID: https://orcid.org/0000-0001-9918-8223, Jiang, Hengle and Zhang, Jingchao 2020. Machine learning and artificial neural network accelerated computational discoveries in materials science. Wiley Interdisciplinary Reviews: Computational Molecular Science 10 (3) , e1450. 10.1002/wcms.1450

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Abstract

Artificial intelligence (AI) has been referred to as the “fourth paradigm of science,” and as part of a coherent toolbox of data‐driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers, but also those work in computational materials science. The number of ML and artificial neural network (ANN) applications in the computational materials science is growing at an astounding rate. This perspective briefly reviews the state‐of‐the‐art progress in some supervised and unsupervised methods with their respective applications. The characteristics of primary ML and ANN algorithms are first described. Then, the most critical applications of AI in computational materials science such as empirical interatomic potential development, ML‐based potential, property predictions, and molecular discoveries using generative adversarial networks (GAN) are comprehensively reviewed. The central ideas underlying these ML applications are discussed, and future directions for integrating ML with computational materials science are given. Finally, a discussion on the applicability and limitations of current ML techniques and the remaining challenges are summarized.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Physics and Astronomy
Publisher:	Wiley
ISSN:	1759-0884
Date of First Compliant Deposit:	19 February 2020
Date of Acceptance:	3 October 2019
Last Modified:	26 Nov 2024 17:00
URI:	https://orca.cardiff.ac.uk/id/eprint/129553

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