Machine learning for interatomic potential models.

By Tim Mueller, Alberto Hernandez, Chuhong Wang. Journal of Chemical Physics, 2020

308 citations recorded by the source corpus.

The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal providing an overview of some of the leading methods in this field. In this perspective, we provide an updated discussion of recent developments, emerging trends, and promising areas for future research in this field. We include in this discussion an overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6169aadcb008f550bd634057ee31fcbfbed3e65b

How researchers see this paper

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In Computational Theory and Mathematics

1. Xianglin Liu
2. Zongrui Pei
3. Jiaxin Zhang

In Materials Chemistry

1. T. Ala‐Nissila
2. Haikuan Dong
3. Zezhu Zeng
4. Zheyong Fan
5. Cunzhi Zhang
6. Yue Chen
7. Yanzhou Wang
8. Xianglin Liu

In Molecular Biology

1. Xianglin Liu
2. Zongrui Pei
3. Jiaxin Zhang

Citations in the corpus

• Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, background): “While several excellent reviews have already been available on the broader topic of ML for materials science [75, 76, 77, 78, 79, 80, 67, 66], there is a lack of comprehensive reviews for the exciting progress and huge potential of ML for HEAs.”
• Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport (2021, background): “[3] Tim Mueller, Alberto Hernandez, and Chuhong Wang, “Machine learning for interatomic potential models,” The Journal of Chemical Physics 152, 050902 (2020).”