A Review of Machine Learning Potentials in the Study of Materials Properties

LI Jinlong; WANG Hao; GENG Huayun

doi:10.11858/gywlxb.20251172

Volume 40 Issue 1

Jan 2026

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Chinese Journal of High Pressure Physics > 2026 > 40(1): 010102.

LI Jinlong, WANG Hao, GENG Huayun. A Review of Machine Learning Potentials in the Study of Materials Properties[J]. Chinese Journal of High Pressure Physics, 2026, 40(1): 010102. doi: 10.11858/gywlxb.20251172

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LI Jinlong, WANG Hao, GENG Huayun. A Review of Machine Learning Potentials in the Study of Materials Properties[J]. Chinese Journal of High Pressure Physics, 2026, 40(1): 010102. doi: 10.11858/gywlxb.20251172

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A Review of Machine Learning Potentials in the Study of Materials Properties

doi: 10.11858/gywlxb.20251172

LI Jinlong,
WANG Hao^{*
,
,},
GENG Huayun^{*
,
,}

Institute of Fluid Physics, CAEP, Mianyang 621999, Sichuan, China

Received Date: 26 Aug 2025
Rev Recd Date: 11 Oct 2025

Available Online: 29 Oct 2025

Issue Publish Date: 05 Jan 2026

Abstract

Abstract

With the rapid advancement of artificial intelligence (AI) technologies and hardware capabilities, AI has gradually become a revolutionary tool driving transformative changes across multiple scientific research domains. In the field of materials science, machine learning methods are significant in high-throughput materials design and property prediction. Over the past decade, machine learning-based approaches for constructing interatomic potentials have been widely applied in the study of material properties, and are providing crucial support for the theoretical design of novel materials and in-depth understanding of their underlying microscopic mechanisms. This article reviews the development of machine learning potentials, and introduces their fundamental workflows. The principles of mainstream methods and their applications in materials property research are outlined. Moreover, recent progress in emerging universal potential models is briefly discussed, then concludes with an analysis of current challenges and future research directions.
- machine learning potential,
- material properties,
- universal potential model

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References(176)

References

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A Review of Machine Learning Potentials in the Study of Materials Properties

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