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Updated: Jul 23, 2025

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基于强化学习的混合债券顺序粗粒度原子间潜力,用于探索液体-液体混合物的中层聚合

Anirban Chandra^1,2, Troy Loeffler^1,2, Henry Chan^1,2

¹Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, USA.

The Journal of chemical physics

|July 11, 2023

概括

我们开发了一种混合债券顺序粗粒度力场 (HyCG) 来建模大尺度聚合. 这种可解释的模型,通过强化学习进行训练,准确地捕捉了液体-液体系统中的关键波动.

科学领域:

计算化学是一种计算化学.
材料科学是一种材料科学.
统计力学就是统计力学.

背景情况:

由于时间尺度的局限性,大尺度物理现象对全原子分子动力学模拟具有挑战性.
粗粒化提供了一个解决方案,通过减少分辨率,同时保持分子结构,与连续的方法不同.

研究的目的:

开发一种新的粗粒度力场,用于模拟中大尺度聚合现象.
为液体液体混合物创建一个可解释和准确的模型.

主要方法:

引入了一种混合债券订单粗粒度力场 (HyCG).
使用连续动作蒙特卡罗树搜索 (cMCTS),强化学习 (RL) 算法参数化了HyCG潜力.
使用来自全原子模拟的数据来训练模型.

主要成果:

开发的RL-HyCG模型准确地描述了二元液体-液体提取系统中中等尺度的关键波动.
cMCTS算法有效地捕获了在训练数据中未明确包含的分子几何特性.

结论:

RL-HyCG模型为中大尺度模拟提供了可解释和准确的方法.
这种方法和培训工作流可以扩展到研究其他超大尺度现象,这些现象无法用于全原子模拟.

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