Advances in high-pressure materials discovery enabled by machine learning

Wang Zhenyu; Luo Xiaoshan; Wang Qingchang; Ge Heng; Gao Pengyue; Zhang Wei; Lv Jian; Wang Yanchao

doi:10.1063/5.0255385

Volume 10 Issue 3

May 2025

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Wang Zhenyu, Luo Xiaoshan, Wang Qingchang, Ge Heng, Gao Pengyue, Zhang Wei, Lv Jian, Wang Yanchao. Advances in high-pressure materials discovery enabled by machine learning[J]. Matter and Radiation at Extremes, 2025, 10(3): 033801. doi: 10.1063/5.0255385

Citation:

Wang Zhenyu, Luo Xiaoshan, Wang Qingchang, Ge Heng, Gao Pengyue, Zhang Wei, Lv Jian, Wang Yanchao. Advances in high-pressure materials discovery enabled by machine learning[J]. Matter and Radiation at Extremes, 2025, 10(3): 033801. doi: 10.1063/5.0255385

Citation:

Wang Zhenyu, Luo Xiaoshan, Wang Qingchang, Ge Heng, Gao Pengyue, Zhang Wei, Lv Jian, Wang Yanchao. Advances in high-pressure materials discovery enabled by machine learning[J]. Matter and Radiation at Extremes, 2025, 10(3): 033801. doi: 10.1063/5.0255385

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Advances in high-pressure materials discovery enabled by machine learning

doi: 10.1063/5.0255385

Wang Zhenyu^{1, 2
,},
Luo Xiaoshan^{1, 3
,},
Wang Qingchang¹,
Ge Heng¹,
Gao Pengyue^1
,,
Zhang Wei^{1
,
,},
Lv Jian^{1
,
,
,},
Wang Yanchao^1
,

1.
Key Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, People’s Republic of China
2.
International Center of Future Science, Jilin University, Changchun 130012, People’s Republic of China
3.
State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, People’s Republic of China

More Information

Corresponding author: a)Authors to whom correspondence should be addressed: zhangw_bxx@jlu.edu.cn and lvjian@jlu.edu.cn
Received Date: 2024-12-29
Accepted Date: 2025-02-24

Available Online: 2025-11-28

Publish Date: 2025-05-01

Abstract

Abstract

Crystal structure prediction (CSP) is a foundational computational technique for determining the atomic arrangements of crystalline materials, especially under high-pressure conditions. While CSP plays a critical role in materials science, traditional approaches often encounter significant challenges related to computational efficiency and scalability, particularly when applied to complex systems. Recent advances in machine learning (ML) have shown tremendous promise in addressing these limitations, enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions. This review provides a concise overview of recent progress in ML-assisted CSP methodologies, with a particular focus on machine learning potentials and generative models. By critically analyzing these advances, we highlight the transformative impact of ML in accelerating materials discovery, enhancing computational efficiency, and broadening the applicability of CSP. Additionally, we discuss emerging opportunities and challenges in this rapidly evolving field.

The authors have no conflicts to disclose.
Conflict of Interest
Zhenyu Wang: Investigation (equal); Writing – original draft (equal); Writing – review & editing (equal). Xiaoshan Luo: Investigation (equal); Writing – original draft (equal); Writing – review & editing (equal). Qingchang Wang: Investigation (equal); Writing – review & editing (equal). Heng Ge: Investigation (equal); Writing – review & editing (equal). Pengyue Gao: Investigation (equal); Writing – review & editing (equal). Wei Zhang: Conceptualization (equal); Supervision (equal); Writing – review & editing (equal). Jian Lv: Conceptualization (equal); Supervision (equal); Writing – original draft (equal); Writing – review & editing (equal). Yanchao Wang: Conceptualization (equal); Supervision (equal); Writing – review & editing (equal).
Author Contributions
The data that support the findings of this study are available from the corresponding authors upon reasonable request.

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