Machine Learning-Guided Approach for Studying Solvation Environments
Machine Learning-Guided Approach for Studying Solvation Environments
复制标题
DOI：
10.1021/acs.jctc.9b00605
复制
发表时间：
2020-01-01
影响因子：
5.5
通讯作者：
Keith, John A.
中科院分区：
文献类型：
--
作者：
Basdogan, Yasemin;Groenenboom, Mitchell C.;Keith, John A.
在化学、生物学和工程学中，经常需要对溶剂化环境进行分子水平的理解和表征。对于任何溶质在任何溶剂中的局部溶剂化效应的实际建模，我们报告了一个静态和全量子力学为基础的集群连续方法计算单离子溶剂化自由能。该方法采用全局优化的方法来识别具有不同数量的显式溶剂分子的低能分子团簇，然后采用原子位置学习核的平滑重叠来量化不同低能溶质环境之间的相似性。从这些数据中，我们使用草图，一个非线性降维算法，获得一个二维的可视化表示的溶质环境之间的相似性在不同大小的microsolvated集群。在对具有2+，1+，1-和2-电荷的不同离子测试这种方法后，我们发现每个离子周围的溶剂化环境通常与其计算的单离子溶剂化自由能变得更加相似。不需要动力学模拟或局部溶剂化结构的离子的先验知识，这种方法可以用来计算溶剂化自由能在5%的实验测量的大多数情况下，它应该是可转移的其他系统的动力学模拟的研究是不容易进行。
Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, we report a static and all-quantum mechanics-based cluster-continuum approach for calculating single-ion solvation free energies. This approach uses a global optimization procedure to identify low-energy molecular clusters with different numbers of explicit solvent molecules and then employs the smooth overlap for atomic positions learning kernel to quantify the similarity between different low-energy solute environments. From these data, we use sketch maps, a nonlinear dimensionality reduction algorithm, to obtain a two-dimensional visual representation of the similarity between solute environments in differently sized microsolvated clusters. After testing this approach on different ions having charges 2+, 1+, 1-, and 2-, we find that the solvation environment around each ion can be seen to usually become more similar in hand with its calculated single-ion solvation free energy. Without needing either dynamics simulations or an a priori knowledge of local solvation structure of the ions, this approach can be used to calculate solvation free energies within 5% of experimental measurements for most cases, and it should be transferable for the study of other systems where dynamics simulations are not easily carried out.