Advances in high-pressure materials discovery enabled by machine learning

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

doi:10.1063/5.0255385

Matter and Radiation at Extremes, Volume. 10, Issue 3, 033801(2025)

Advances in high-pressure materials discovery enabled by machine learning

Zhenyu Wang^1,2、*, Xiaoshan Luo^1,3, Qingchang Wang¹, Heng Ge¹, Pengyue Gao¹, Wei Zhang¹, Jian Lv¹, and Yanchao Wang¹

¹Key Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, People’s Republic of China

²International Center of Future Science, Jilin University, Changchun 130012, People’s Republic of China

³State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, People’s Republic of China

show less

Figures & Tables(1)

Fig. 1. Schematic representation of crystal structure prediction, machine learning potentials, and crystal generative models, and their interrelations.

Download full size

View in Article

Tools

Get Citation

Copy Citation Text

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

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Received: Dec. 29, 2024

Accepted: Feb. 24, 2025

Published Online: Jul. 16, 2025

The Author Email:

DOI:10.1063/5.0255385

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology