物理学进展

物理学进展 ›› 2024, Vol. 44 ›› Issue (2): 49-72.doi: 10.13725/j.cnki.pip.2024.02.001

所属专题： 2023年, 第43卷

• •    下一篇

温稠密物质模拟的第一性原理方法进展

张 航1.2, 陈默涵1.3   

  1. 1. 高能量密度物理数值模拟教育部重点实验室，北京大学应用物理与技术研究中心，北京大学工学院，北京 100871 ; 2. 北京大学物理学院，北京 100871 ; 3. 北京科学智能研究院，北京 100080
  • 出版日期:2024-04-20 发布日期:2024-05-08
  • 基金资助:
    冲击波物理与爆轰物理全国重点实 验室稳定支持科研项目 JCKYS2022212010、国家自然 科学基金委 (项目号 12122401，12074007，12135002)、 北京大学新工科交叉青年专项

Progress in First-Principles Methods for Simulation of Warm Dense Matter

ZHANG Hang1.2, CHEN Mo-han1.3   

  1. 1. HEDPS, Center for Applied Physics and Technology, College of Engineering, Peking University, Beijing 100871, China; 2. School of Physics, Peking University, Beijing 100871, China; 3. AI for Science Institute, Beijing 100080, China
  • Online:2024-04-20 Published:2024-05-08

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摘要： 温稠密物质 (Warm Dense Matter, WDM) 是介于凝聚态物质和等离子体之间的一种过 渡状态的物质，也是行星物理、实验室天体物理和惯性约束聚变等高能量密度物理领域的前沿科 研方向。温稠密物质的量子效应显著，具有部分电离、强耦合、电子简并和热效应等重要的物理 性质，因此需要采用量子力学的基础理论来描述。近年来，基于量子力学的第一性原理计算模拟 方法发展迅速，逐渐成为了深入理解温稠密物质性质的有效工具。一方面，直接将凝聚态物理和 材料科学中广泛适用的第一性原理方法应用于温稠密物质面临着巨大的挑战，特别是在宽温区和 极端高压等极端条件下，需要不断改进现有的第一性原理算法和软件。另一方面，基于机器学习 的分子动力学方法发展迅速，也给温稠密物质模拟带来了新的工具。在这篇综述中，我们首先回 顾了适用于温稠密物质模拟的传统第一性原理方法，包括 Kohn-Sham 密度泛函理论方法和无轨 道密度泛函理论方法。其次，我们介绍了近年来发展的新方法和软件，例如改进的第一性原理方 法和随机密度泛函理论方法，后者已在国产开源密度泛函理论软件原子算筹 (ABACUS) 中实现。 以上新方法可以显著提升温稠密物质的计算规模和效率，从而提升温稠密物质的结构、动力学和 输运系数等性质的计算精度。

关键词: 温稠密物质, 第一性原理, 分子动力学, 机器学习, 原子算筹

Abstract: Warm Dense Matter (WDM) represents a transitional state of matter situated between condensed matter and plasma, emerging as a cutting-edge research direction within the realms of planetary physics, laboratory astrophysics, and inertial confinement fusion in the field of high-energy density physics. WDM is characterized by significant quantum effects, partial ionization, strong coupling, electron degeneracy, and thermal effects, necessitating a description based on fundamental quantum mechanical theories. In recent years, simulations and calculations based on quantum mechanics’ first principles have rapidly advanced, increasingly becoming an effective tool for a deeper understanding of WDM properties. On one hand, applying First Principles widely used in condensed matter physics and materials science to WDM poses considerable challenges, especially under extreme conditions such as broad temperature ranges and high pressures, which require continuous improvements to existing first-principle algorithms and software. On the other hand, the rapid development of machine learning-based molecular dynamics methods offers new tools for simulating WDM. In this review, we initially revisit traditional first principles applicable to WDM simulations, including Kohn-Sham Density Functional Theory and Orbital-free Density Functional Theory. Subsequently, we introduce newly developed methods and software, such as Extended First Principles Molecular Dynamics and Stochastic Density Functional Theory, the latter of which has been implemented in the domestically developed open-source density functional theory software, Atomic-orbital Based Ab-initio Computation at UStc (ABACUS). These innovative approaches significantly boost the computational scale and efficiency of WDM studies, thereby elevating the precision of structural, dynamical, and transport coefficient calculations related to WDM

Key words: warm dense matter, first principle, molecular dynamic, machine learning, Atomicorbital Based Ab-initio Computation at UStc (ABA

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