Willman Jonathan T, Gonzalez Joseph M, Nguyen-Cong Kien, Hamel Sebastien, Lordi Vincenzo, Oleynik Ivan I
Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
Department of Physics, University of South Florida, Tampa, Florida 33620, USA.
J Chem Phys. 2024 Aug 28;161(8). doi: 10.1063/5.0218705.
Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. This study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure-temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.
大规模原子分子动力学（MD）模拟为深入理解高压高温极端条件下的碳提供了绝佳机会。然而，这些模拟的保真度在很大程度上取决于控制多原子系统动力学的经典原子间势的准确性。本研究批判性地评估了几种常用的碳经验势以及机器学习原子间势（MLIPs）在模拟高压高温下一系列物理性质的能力，包括金刚石状态方程、其熔点线、冲击雨贡纽曲线、单轴压缩以及液态碳的结构。经验势无法准确预测碳在高压高温条件下的行为。相比之下，MLIPs表现出量子精度，其中光谱邻域分析势（SNAP）和原子团簇展开（ACE）在重现密度泛函理论结果方面最为准确。尽管ACE没有针对极端条件进行专门训练，但它具有显著的可转移性。此外，在基于图形处理单元的系统上进行的数十亿原子MD模拟中，ACE和SNAP展现出卓越的计算性能，其中SNAP速度最快。除了在选择具有精度、可转移性和计算效率良好平衡的原子间势方面提供实际指导外，这项工作还突出了利用MLIPs在新兴的百亿亿次超级计算机上进行量子精确MD模拟所带来的突破性科学发现的变革性机遇。
