Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic
Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic
复制标题
使用欧拉特性进行分子动力学模拟的拓扑分析
DOI：
10.1021/acs.jctc.2c00766
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发表时间：
2023
影响因子：
5.5
通讯作者：
Zavala, Victor M.
中科院分区：
文献类型：
--
作者：
Smith, Alexander;Runde, Spencer;Chew, Alex K.;Kelkar, Atharva S.;Maheshwari, Utkarsh;Van Lehn, Reid C.;Zavala, Victor M.
分子动力学（MD）模拟用于不同的科学和工程领域，如药物发现，材料设计，分离，生物系统和反应工程。这些模拟生成了高度复杂的数据集，这些数据集捕获了数千个分子的3D空间位置，动力学和相互作用。分析MD数据集是理解和预测紧急现象以及识别关键驱动因素和调整此类现象的设计旋钮的关键。在这项工作中，我们表明，欧拉特征（EC）提供了一个有效的拓扑描述符，便于MD分析。EC是一个通用的，低维的，易于解释的描述符，可用于减少，分析和量化表示为图形/网络，流形/函数和点云的复杂数据对象。具体来说，我们表明EC是一个信息丰富的描述符，可用于机器学习和数据分析任务，如分类，可视化和回归。我们证明了所提出的方法的好处，通过案例研究，旨在了解和预测自组装单分子膜的疏水性和复杂溶剂环境的反应性。
Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex data sets that capture the 3D spatial positions, dynamics, and interactions of thousands of molecules. Analyzing MD data sets is key for understanding and predicting emergent phenomena and in identifying key drivers and tuning design knobs of such phenomena. In this work, we show that the Euler characteristic (EC) provides an effective topological descriptor that facilitates MD analysis. The EC is a versatile, low-dimensional, and easy-to-interpret descriptor that can be used to reduce, analyze, and quantify complex data objects that are represented as graphs/networks, manifolds/functions, and point clouds. Specifically, we show that the EC is an informative descriptor that can be used for machine learning and data analysis tasks such as classification, visualization, and regression. We demonstrate the benefits of the proposed approach through case studies that aim to understand and predict the hydrophobicity of self-assembled monolayers and the reactivity of complex solvent environments.