Machine Learning-Guided Approach for Studying Solvation Environments

Abstract

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.

Global optimization of chemical cluster structures: Methods, applications, and challenges

“…The first step in uncovering the catalytic roles of the nanoparticles Pt 24 [261] and [262, 263] was also made via ABCluster structure determination. To model liquid‐phase reactions, ABCluster has been used to construct clusters of reaction intermediates and solvent molecules [264–266], for example, in the study of electrochemically catalyzed Newman−Kwart rearrangement [267].…”

Solvation Free Energy Prediction from Pairwise Atomistic Interactions by Machine Learning

“…The importance of solvation or hydration mechanisms and their accompanying free energy change has rendered in silico calculation methods for the solvation energy one of the most important applications in computational chemistry [33,16,69,15,37,32,61,11,40,35,38,53,34,41,31,63,76,19,14,17,75,6,50,21,12,3,48,54,67,46]. Solvation free energy directly influences numerous chemical properties in condensed phases and plays a dominant role in various chemical reactions, such as drug delivery [16,63,19,47], organic synthesis [49], electrochemical redox reactions [68,43,1,28], etc.…”

Recent Progress in the Development of Screening Methods to Identify Electrode Materials for the Oxygen Evolution Reaction

“…[100] The motivated data-mining activities in conjunction with machine-learning techniques may spur further progress in computational electrocatalysis, as also evident from the recent literature. [101][102][103] A pressing issue in electrocatalysis, particularly for the OER in an acidic electrolyte, corresponds to the aspect of catalyst stability. Regrettably, the most active OER materials, such as RuO 2 , lack long-term stability, restricting their use for practical applications.…”

“…[ 100 ] The motivated data‐mining activities in conjunction with machine‐learning techniques may spur further progress in computational electrocatalysis, as also evident from the recent literature. [ 101–103 ]…”