论文标题
在极端条件下模拟碳的机器学习间潜力
Machine Learning Interatomic Potential for Simulations of Carbon at Extreme Conditions
论文作者
论文摘要
已经开发了用于在极端压力（最高5 tpa）和温度（高达20,000 K）处的碳模拟的频谱邻域分析（SNAP）机间潜能（MLIP）。这是使用大型实验相关量子分子动力学（QMD）数据的大型数据库，使用强大的机器学习方法训练SNAP势，并对QMD和实验数据进行广泛的验证。最终的碳MLIP在预测碳相图，晶体相和休克Hugoniot的熔融曲线方面表现出了前所未有的准确性和可传递性。通过在领导级高性能计算系统上实现量子准确性和有效的实施，通过在实验时间和长度尺度上启用原子级见解，来促进经典MD模拟的前沿。
A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed for simulations of carbon at extreme pressures (up to 5 TPa) and temperatures (up to 20,000 K). This was achieved using a large database of experimentally relevant quantum molecular dynamics (QMD) data, training the SNAP potential using a robust machine learning methodology, and performing extensive validation against QMD and experimental data. The resultant carbon MLIP demonstrates unprecedented accuracy and transferability in predicting the carbon phase diagram, melting curves of crystalline phases, and the shock Hugoniot, all within 3% of QMD. By achieving quantum accuracy and efficient implementation on leadership class high performance computing systems, SNAP advances frontiers of classical MD simulations by enabling atomic-scale insights at experimental time and length scales.
