在RMCProfile中集成机器学习的原子间潜力与混合逆蒙特卡洛结构的改进
Paul Cuillier1, Matthew G Tucker2, Yuanpeng Zhang2
1Department of Materials Science and Engineering The Ohio State University Columbus OH43212 USA.
概括
混合逆蒙特卡洛 (RMC) 模拟现在通过LAMMPS结合机器学习的原子间潜力. 这扩大了RMC的规模.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 晶体学 晶体学是指结晶学.
背景情况:
- 反向蒙特卡洛 (RMC) 对于解释衍射数据至关重要.
- 混合RMC使用原子间潜力获得物理现实的结果.
- 目前的混合RMC方法受到可用的原子间电位的限制.
研究的目的:
- 通过整合更广泛的原子间潜能来增强混合RMC方法.
- 为了研究缺乏先前存在的原子间潜力的材料.
- 开发一种用于训练混合RMC的机器学习原子间潜力的方法.
主要方法:
- 在RMCProfile.中实现了一个新的原子间潜力约束.
- 综合支持来自大型原子/分子大规模并行模拟器 (LAMMPS) 的潜力.
- 开发了一个工作流程来训练机器学习使用RMC的原子间潜力.
主要成果:
- 成功扩展混合RMC功能,包括LAMMPS支持的潜力.
- 在混合RMC框架内实现机器学习原子间潜力的应用.
- 展示了一种创新方法,用于为RMC创建定制的原子间潜力.
结论:
- 增强的混合RMC方法显著扩大了材料研究的范围.
- 机器学习的原子间潜力为RMC结构改进提供了灵活的方法.
- 这项工作为将混合RMC应用于更广泛的复杂材料铺平了道路.
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