The evolution of machine learning potentials for molecules, reactions and materials

Junfan Xia; Yaolong Zhang; Bin Jiang

doi:10.1039/d5cs00104h

journal article Jan 01, 2025

The evolution of machine learning potentials for molecules, reactions and materials

Junfan Xia

Yaolong Zhang Bin Jiang

Chemical Society Reviews Vol. 54 No. 10 pp. 4790-4821 · Royal Society of Chemistry (RSC)

View at Publisher Save 10.1039/d5cs00104h

Abstract

This review offers a comprehensive overview of the development of machine learning potentials for molecules, reactions, and materials over the past two decades, evolving from traditional models to the state-of-the-art.

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Details

Published: Jan 01, 2025
Vol/Issue: 54(10)
Pages: 4790-4821
License: View

Authors

J

Junfan Xia

State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China; School of Chemistry and Materials Science, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China

Y

Yaolong Zhang

Department of Chemistry and Chemical Biology, Center for Computational Chemistry, University of New Mexico, Albuquerque, New Mexico 87131, USA

B

Bin Jiang

State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China; School of Chemistry and Materials Science, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China; Hefei National Laboratory, University of Science and Technology of China, Hefei, 230088, China

Funding

National Natural Science Foundation of China Award: 22325304

Chinese Academy of Sciences Award: XDB0450101

National Center of Competence in Research Quantum Science and Technology Award: 2021ZD0303301

Cite This Article

Junfan Xia, Yaolong Zhang, Bin Jiang (2025). The evolution of machine learning potentials for molecules, reactions and materials. Chemical Society Reviews, 54(10), 4790-4821. https://doi.org/10.1039/d5cs00104h

The evolution of machine learning potentials for molecules, reactions and materials

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