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OA
Advances in the Application of Machine Learning Potential to the Calculation of Mineral States and Properties in the Earth's Deep Interior
地球深部处于极端高温高压环境,其物质组成、相变行为和物理性质对于理解地球内部结构、动力学过程及演化具有重要意义.在极端条件下,传统实验手段面临热力学状态难以维持、物理量诊断困难的挑战,而第一性原理计算虽然具有量子精度,却受限于计算效率,难以直接应用于大时空尺度的地球深部矿物模拟.机器学习方法带来了新的机遇,基于第一性原理精度的数据集构建的高精度、高效率的机器学习势函数,显著拓展了第一性原理模拟的时空尺度,为研究地球深部矿物的物态、相变、弹性、输运等性质提供了革命性工具.系统地综述了机器学习方法在地球深部主要矿物(包括上地幔、过渡带与下地幔矿物、俯冲带组分以及地核物质)研究中的应用进展,总结了其在揭示相变、热导率、扩散、熔化和弹性性质等方面的代表性成果,并探讨了当前研究存在的局限性及未来发展方向.
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.
WANG Chuan;ZENG Qiyu;CHEN Bo;YU Xiaoxiang;KANG Dongdong;DAI Jiayu
College of Science,National University of Defense Technology,Changsha 410073,Hunan,ChinaCollege of Advanced Interdisciplinary Studies,National University of Defense Technology,Changsha 410073,Hunan,ChinaCollege 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,ChinaCollege 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,ChinaCollege 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,ChinaCollege 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
数理科学
地球深部矿物物态物性机器学习第一性原理分子动力学
Earth's deep interiormineralsphysical states and propertiesmachine learningfirst-principlesmolecular dynamics
《高压物理学报》 2026 (1)
2-19,18
国防科技大学自主科研基金国家自然科学基金(12504326,12304307,12104507)湖南省科技创新项目(2025ZYJ001,2021RC4026)
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