通过机器学习进行自动化过程探索 辅助过渡状态搜索
King Chun Lai1, Patricia Poths1, Sebastian Matera1
1Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany.
Physical review letters
|March 25, 2025
概括
一个自动过程探索器 (APE) 框架使用机器学习来发现新的低障碍扩散过程. 这显著增强了动力蒙特卡洛 (kMC) 模拟,揭示了以前被忽视的原子机制.
科学领域:
- 计算材料科学 计算材料科学
- 表面科学是一门科学.
- 化学物理 化学物理
背景情况:
- 动力蒙特卡洛 (kMC) 模拟通常依赖于预定义的基本过程,限制了发现.
- 人类的直觉可能是识别复杂系统的所有相关原子过程的瓶.
研究的目的:
- 引入一个高效的自动流程探索器 (APE) 框架.
- 在定义模拟过程列表时克服人类直觉的局限性.
- 为了提高kMC模拟的准确性和范围.
主要方法:
- 开发了一个APE框架,使用模糊的机器学习分类算法.
- 通过针对尚未探索的原子环境,尽量减少过渡状态搜索中的冗余.
- 应用APE在Pd100) 表面上研究岛屿扩散.
主要成果:
- APE发现了大量以前被忽视的低障碍集体扩散过程.
- 与传统模型相比,这些集体过程显著增加了岛屿的扩散性.
- 该框架有效地探索原子配置,减少冗余搜索.
结论:
- APE框架提供了一种高效的,自动化的方法来发现基本过程.
- 它通过包括更广泛的原子机制来增强kMC模拟的预测能力.
- 这种方法对于准确建模表面扩散和其他复杂现象至关重要.
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