Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction

Chengxi Zhao; Zhaojia Ma; Daojin Fan; Siyu Hu; Leping Wang; Feng Hua; Weile Jia; En Shao; Guangming Tan; Jun Jiang; Linjiang Chen

doi:10.1039/d6dd00016a

January 1, 2025Digital DiscoveryOpen Access

Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction

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Key Points

This research aims to enhance crystal structure prediction using machine learning interatomic potentials and optimization techniques.
Developed a machine learning model for interatomic potentials.
Employed a batched optimization approach to sample packing landscapes.
Analyzed energy differences within molecular crystal structures.
Significant reduction in computational resources needed for crystal structure prediction.
Improved accuracy in resolving energy differences between competing structures.
Demonstrated efficiency across various molecular systems.

Implication

Integration of machine learning and optimization improves crystal structure prediction by reducing computational barriers.

Abstract

Molecular crystal structure prediction (CSP) faces a persistent computational bottleneck: it requires exhaustive sampling of vast packing landscapes while resolving energy differences of only a few kJ/mol. We introduce BOMLIP-CSP,...

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Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/69d0aee0659487ece0fa4c61 https://doi.org/10.1039/d6dd00016a

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Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

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