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GraphVAMP用于发现自组装动态的缓慢集体变量

Bojun Liu¹, Mingyi Xue¹, Yunrui Qiu¹

¹Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.

The Journal of chemical physics

|September 1, 2023

概括

我们介绍了GraphVAMPnets,这是一种使用图形神经网络和VAMP理论的新方法,用于识别分子自我组装动态中的关键慢集体变量 (CV),克服现有技术的局限性.

科学领域:

计算化学和材料科学计算化学和材料科学
统计力学和动态系统的统计力学.
机器学习用于科学发现.

背景情况:

识别慢集体变量 (CV) 对于理解自组装动力学和设计新材料至关重要.
现有的方法在同样的单体的换/旋转对称性和自我组装中分离途径的样本采集不足方面扎.
强加细节平衡的尺寸缩小技术可以掩盖真正的自组装动态.

研究的目的:

开发一种强大的计算方法,用于识别分子自组合中的缓慢CV.
解决与自组装过程中的对称性不变性和失平衡动态相关的挑战.
为阐明运动路径和指导自组装材料自下而上的设计提供一个工具.

主要方法:

采用GraphVAMPnets,这是一个混合方法,将图形神经网络 (GNN) 与马科夫过程 (VAMP) 理论的变化方法相结合.
使用GNN来实现自组装结构的高分辨率,对称不变的特征表示.
应用VAMP理论来分析马尔科夫过程而不强制执行详细的平衡约束,适用于非平衡动态.

主要成果:

在两个模型系统的自组动力学中,GraphVAMPnets成功地确定了缓慢的CV:疏水分子聚合和不齐的粒子聚合.
该方法证明了与自组装系统固有的 permutation 和旋转对称性的不变性.
该方法有效地应对了自组装动态中逆向转换采样不足的挑战.

结论:

GraphVAMPnets提供了一种强大的新方法,用于发现分子自我组装中的缓慢集体变量.
这种方法克服了传统技术的关键局限性,使得自组装动力学能够更准确地分析.
该框架预计将在理解和设计各种系统的自组装材料方面具有广泛的适用性.

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