通过匹配热力学力进行多尺度粗粒度的最大积理论:应用于分子晶体 (TATB)
Sergei Izvekov1, Matthew P Kroonblawd2, James P Larentzos1
1U.S. Army DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States.
The journal of physical chemistry. B
|March 15, 2024
概括
我们使用最大 (PME) 原理开发了一种新的理论,用于通过力匹配 (MSCG/FM) 进行多尺度粗粒化. 这种增强的方法提高了像TATB这样的异性质材料在亚分子水平上的粗粒度.
科学领域:
- 计算材料科学科学 计算材料科学
- 机器学习在化学中的应用
- 统计力学 统计力学
背景情况:
- 通过力匹配 (MSCG/FM) 进行多尺度粗粒化是一种有效的监督机器学习技术,用于创建微观信息粗粒 (CG) 模型.
- 现有的MSCG/FM方法可以在更广泛的理论框架下统一和扩展.
研究的目的:
- 提出一种基于最大 (PME) 原理的新理论,包括现有的MSCG/FM方法.
- 扩展MSCG/FM方法,以改善异性质材料的粗粒度.
主要方法:
- 开发了一个基于最大 (PME) 原理的理论来概括MSCG/FM.
- 制定了结合和非结合相互作用的一致匹配.
- 将MSCG/FM扩展到对异性质介质的恒定应变组合.
- 将该方法应用于晶体1,3,5-三氨基-2,4,6-三丁二 (TATB) 的细粗粒度.
主要成果:
- 基于PME的理论为MSCG/FM提供了一个统一的观点,将其视为匹配热力学力的特殊情况.
- 扩展的MSCG/FM方法,包括常量应变组合和明确的结合力匹配,适用于异构介质.
- 证明了TATB的成功细粗粒度,这是一种异性质能量材料.
结论:
- 基于PME的理论为开发先进的CG模型提供了一个强大的框架.
- 增强的MSCG/FM方法能够准确地分辨异性质材料的亚分子分辨率.
- 这项工作促进了机器学习在复杂系统的材料科学中的应用.
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