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低分辨率粗粒度自由能量潜力的层次机器学习

Sergei Izvekov1, Betsy M Rice1

  • 1U.S. Army DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States.

Journal of chemical theory and computation
|May 31, 2023
PubMed
概括

本研究介绍了一种机器学习方法,用于创建粗粒度模型,探索聚类如何影响自由能源潜力. 这种方法可以有效地构建层次模型,而不会损失精度或增加计算成本.

科学领域:

  • 计算化学和材料科学.
  • 机器学习和统计力学.
  • 多尺度建模和模拟. 多尺度建模和模拟.

背景情况:

  • 开发精确的粗粒度 (CG) 模型需要热力学和统计学等效的潜力,以参考显微模型.
  • 在超分子尺度上的粗粒度需要对非结合粒子进行面向目标的聚类,使得缩小的描述取决于聚类算法.
  • 现有的CG自由能量 (FE) 潜力的监督机器学习 (ML) 方法,如通过力匹配 (MSCG/FM) 的多尺度粗粒化,是高效的,但可以通过集群选择受到影响.

研究的目的:

  • 探索机器学习 (ML) 对粗粒度 (CG) 赫尔姆霍尔茨自由能量 (FE) 潜力的依赖性,对不同的集群算法.
  • 开发一种复杂的ML方法,结合聚合集群和MSCG/FM,以高效地构建细到低分辨率的CG模型层次结构.
  • 展示一种方法,可以避免准确性降低和更大的层次结构的计算成本增加,消除CG粒子的上层尺寸限制.

主要方法:

  • 研究了分区 (k-means,Voronoi) 和等级聚合 (bottom-up) 集群算法的对 ML 衍生 CG 赫尔姆霍尔茨 FE 潜力的影响.
  • 开发了MSCG/FM学习潜力和聚类统计数据之间的理论联系.
  • 提出了一种递归的ML方法,将聚合集群与MSCG/FM集成在一起,用于层次模型开发.
  • 通过液甲的全原子分子动力学 (MD) 模拟来证明方法,以获得自下而上的聚合层次结构.

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主要成果:

  • CG Helmholtz FE潜力的ML取决于所选择的集群算法.
  • 结合聚合集群和MSCG/FM的递归方法学有效地创建CG模型的层次结构,而不会影响准确性或计算效率.
  • 对于聚合层次结构来说,已被证明存在重新规范化群体转换,表明自我相似性,并通过重新调整实现低分辨率潜力的低成本学习.
  • 开发的分层CG模型适用于恒压模拟.

结论:

  • 选择集群算法显著影响粗粒度自由能量潜力的机器学习.
  • 一种新的,高效的递归机器学习方法允许在多个分辨率上构建层次的粗粒度模型.
  • 这种方法促进了对复杂系统的准确,可计算的多尺度模型的开发,包括在恒压模拟中的应用.