分子系统中能量和熵途径的量化
Quantifying Energetic and Entropic Pathways in Molecular Systems
摘要：
当研究在非零温度下发生的动力学时，在描述激活的势垒穿越事件时，必须考虑能量和熵。此外，需要构造良好的反应坐标来描述不同的亚稳态以及它们之间的转变机制。在这里，我们使用一种基于物理的机器学习方法，称为状态预测信息瓶颈（SPIB），为三个不同复杂度的系统找到非线性反应坐标。SPIB能够正确地预测分析平能双阱系统的熵瓶颈，并确定分析四阱系统的熵和能量主导的途径。最后，通过模拟苯甲酸透过脂双层的渗透，SPIB能够发现渗透过程中的熵和能量障碍。鉴于这些结果，我们因此建立SPIB是一个合理的和强大的方法，在物理系统中找到重要的熵和能量/焓障碍，然后可以用于增强理解和采样不同的激活机制。
英文摘要：
When examining dynamics occurring at non-zero temperatures, both energy and entropy must be taken into account while describing activated barrier crossing events. Furthermore, good reaction coordinates need to be constructed to describe different metastable states and the transition mechanisms between them. Here we use a physics-based machine learning method called the State Predictive Information Bottleneck (SPIB) to find non-linear reaction coordinates for three systems of varying complexity. The SPIB is able to predict correctly an entropic bottleneck for an analytical flat-energy double-well system and identify the entropy- and energy-dominated pathways for an analytical four-well system. Finally, for a simulation of benzoic acid permeation through a lipid bilayer, SPIB is able to discover the entropic and energetic barriers to the permeation process. Given these results, we thus establish that SPIB is a reasonable and robust method for finding the important entropy and energy/enthalpy barriers in physical systems, which can then be used for enhanced understanding and sampling of different activated mechanisms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
