Advances in the Application of Machine Learning Potential to the Calculation of Mineral States and Properties in the Earth’s Deep Interior

WANG Chuan; ZENG Qiyu; CHEN Bo; YU Xiaoxiang; KANG Dongdong; DAI Jiayu

doi:10.11858/gywlxb.20251218

Volume 40 Issue 1

Jan 2026

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Chinese Journal of High Pressure Physics > 2026 > 40(1): 010101.

WANG Chuan, ZENG Qiyu, CHEN Bo, YU Xiaoxiang, KANG Dongdong, DAI Jiayu. Advances in the Application of Machine Learning Potential to the Calculation of Mineral States and Properties in the Earth’s Deep Interior[J]. Chinese Journal of High Pressure Physics, 2026, 40(1): 010101. doi: 10.11858/gywlxb.20251218

Citation:

WANG Chuan, ZENG Qiyu, CHEN Bo, YU Xiaoxiang, KANG Dongdong, DAI Jiayu. Advances in the Application of Machine Learning Potential to the Calculation of Mineral States and Properties in the Earth’s Deep Interior[J]. Chinese Journal of High Pressure Physics, 2026, 40(1): 010101. doi: 10.11858/gywlxb.20251218

Citation:

WANG Chuan, ZENG Qiyu, CHEN Bo, YU Xiaoxiang, KANG Dongdong, DAI Jiayu. Advances in the Application of Machine Learning Potential to the Calculation of Mineral States and Properties in the Earth’s Deep Interior[J]. Chinese Journal of High Pressure Physics, 2026, 40(1): 010101. doi: 10.11858/gywlxb.20251218

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Advances in the Application of Machine Learning Potential to the Calculation of Mineral States and Properties in the Earth’s Deep Interior

doi: 10.11858/gywlxb.20251218

WANG Chuan^1
,,
ZENG Qiyu²,
CHEN Bo^{1, 3, 4},
YU Xiaoxiang^{1, 3, 4},
KANG Dongdong^{1, 3, 4},
DAI Jiayu^{1, 3, 4,*
,
,}

1.
College of Science, National University of Defense Technology, Changsha 410073, Hunan, China
2.
College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, Hunan, China
3.
Hunan Key Laboratory of Extreme Matter and Applications, National University of Defense Technology, Changsha 410073, Hunan, China
4.
Hunan Research Center of the Basic Discipline for Physical States, National University of Defense Technology, Changsha 410073, Hunan, China

Received Date: 30 Sep 2025
Rev Recd Date: 11 Oct 2025

Available Online: 23 Oct 2025

Issue Publish Date: 05 Jan 2026

Abstract

Abstract

The deep interior of the Earth is under extreme high-temperature and high-pressure conditions. Its material composition, phase transition behavior, and physical properties are crucial for understanding the Earth’s internal structure, dynamic processes, and evolution. Traditional experimental methods face challenges in maintaining thermodynamic states and diagnosing physical quantities under such extreme conditions. While first-principles calculations offer quantum-level precision, their computational efficiency limits their direct application to simulating deep-Earth minerals across large spatiotemporal scales. Machine learning methods present new opportunities. By constructing high-precision, efficient machine learning potentials based on first-principles datasets, machine learning methods significantly extend the spatiotemporal scale of first-principles simulations, which provide revolutionary tools for studying the physical states, phase transitions, elasticity, and transport properties of deep-Earth minerals. This paper systematically reviews the progress of machine learning applications in studying major deep-Earth minerals, including those in the upper mantle, transition zone, lower mantle, subduction zone components, and core materials, and summarizes the representative achievements of machine learning methods in revealing phase transitions, thermal conductivity, diffusion, melting, and elastic properties, while also discussing current limitations and future research directions.
- Earth’s deep interior,
- minerals,
- physical states and properties,
- machine learning,
- first-principles,
- molecular dynamics

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References(144)

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Advances in the Application of Machine Learning Potential to the Calculation of Mineral States and Properties in the Earth’s Deep Interior

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