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机器学习势函数在地球深部矿物物态物性计算中的应用进展

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

高压物理学报2026，Vol.40Issue(1)：2-19,18.
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高压物理学报2026，Vol.40Issue(1)：2-19,18.DOI:10.11858/gywlxb.20251218

机器学习势函数在地球深部矿物物态物性计算中的应用进展

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

WANG Chuan 1ZENG Qiyu 2CHEN Bo 3YU Xiaoxiang 3KANG Dongdong 3DAI Jiayu3

作者信息

  • 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. College of Science,National University of Defense Technology,Changsha 410073,Hunan,China||Hunan Key Laboratory of Extreme Matter and Applications,National University of Defense Technology,Changsha 410073,Hunan,China||Hunan Research Center of the Basic Discipline for Physical States,National University of Defense Technology,Changsha 410073,Hunan,China
  • 折叠

摘要

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.

关键词

地球深部/矿物/物态物性/机器学习/第一性原理/分子动力学

Key words

Earth's deep interior/minerals/physical states and properties/machine learning/first-principles/molecular dynamics

分类

数理科学

引用本文复制引用

WANG Chuan,ZENG Qiyu,CHEN Bo,YU Xiaoxiang,KANG Dongdong,DAI Jiayu..机器学习势函数在地球深部矿物物态物性计算中的应用进展[J].高压物理学报,2026,40(1):2-19,18.

基金项目

国防科技大学自主科研基金 （）

国家自然科学基金(12504326,12304307,12104507) （12504326,12304307,12104507）

湖南省科技创新项目(2025ZYJ001,2021RC4026) （2025ZYJ001,2021RC4026）

高压物理学报

1000-5773

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