强化学习指导的金属中运输的长期模拟
Hao Tang1, Boning Li2,3, Yixuan Song1
1Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|December 7, 2023
概括
强化学习 (RL) 允许在合金中进行扩散的原子模拟,以实现实验时间表. 这种方法揭示了新的空配合运动,并加速了低能配置的采样.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 复杂合金中扩散的原子模拟面临时间尺度的限制,将分析限制在实验相关性较短的持续时间内.
- 了解材料中的原子过程对于设计具有所需性质的新合金至关重要.
研究的目的:
- 开发使用强化学习 (RL) 的长时间尺度模拟方法,以克服原子扩散模拟中的时间尺度问题.
- 实施和解释两个专门的RL算法:RL过渡动力学模拟器 (TKS) 和RL低能状态采样器 (LSS).
主要方法:
- 强化学习 (RL) 技术的开发和应用,用于长时间的原子模拟.
- 实现RL过渡动力学模拟器 (TKS) 模拟扩散过程.
- 实施RL低能状态采样器 (LSS) 以高效地采样材料配置.
主要成果:
- 使用RL TKS计算纯金属和CrCoNi合金中的散率,与实验数据达成良好一致.
- 观察到气和空缺之间的反直觉合作运动,这是扩散机制的新发现.
- 证明RL LSS与传统的Metropolis-Hastings算法相比,显著加快了低能耗配置的采样.
结论:
- 强化学习提供了一种强大的方法,可以在扩散的原子模拟中扩展可访问的时间尺度.
- 开发的RL方法,TKS和LSS,对于研究扩散动力学和探索材料配置是有效的.
- 这项工作为模拟材料科学和相关领域的复杂原子过程开辟了新的途径.
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