机器学习力场辅助集群扩展方法对合金材料的相位图
Jun-Zhong Xie1, Xu-Yuan Zhou1, Bin Jin1
1Beijing National Laboratory for Molecular Sciences, State Key Laboratory of Rare Earth Material Chemistry and Application, Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, 100871 Beijing, China.
Journal of chemical theory and computation
|June 28, 2024
概括
开发准确的合金替代模型是必不可少的. 本研究引入了凸船体保存机器学习力场 (CHP-MLFF) 进行高效的合金模拟,改进相位图预测.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 密度函数理论 (DFT) 对于研究多元合金至关重要,但在计算上昂贵.
- 需要有效的替代模型来模拟无序的合金系统.
- 现有的机器学习力场 (MLFF) 方法往往忽略了关键的物理约束,如凸船体的保存.
研究的目的:
- 开发一个工作流程,用于训练凸船体保存 (CHP) MLFF用于二元合金.
- 使用训练有素的CHP-MLFF和Wang-Landau蒙特卡洛 (WLMC) 模拟来预测秩序-混乱阶段边界.
- 评估凸船体保护对合金系统替代模型准确性的影响.
主要方法:
- 开发了一种工作流程来训练凸船体保存机器学习力场 (CHP-MLFF).
- 采用Wang-Landau蒙特卡洛 (WLMC) 技术进行相位边界预测.
- 使用CHP-MLFF进行比较分析的构建集群扩展 (CE) 模型.
主要成果:
- 预测的顺序-混乱相位过渡温度与实验数据有很好的一致性.
- 通过CHP-MLFF,可以构建准确高效的集群扩展 (CE) 模型.
- 证明了基态配置准确性和相位过渡温度预测之间的强烈相关性.
结论:
- 在合金建模中,对凸船体的保存对于开发精确的替代哈密尔顿人至关重要.
- 拟议的CHP-MLFF工作流提高了相位图预测的可靠性.
- 这种方法提供了一种计算效率高,准确的方法来模拟无序的多元件合金.
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