Abstract
We propose to analyze molecular dynamics (MD) output via a supervised machine learning (ML) algorithm, the decision tree. The approach aims to identify the predominant geometric features which correlate with trajectories that transition between two arbitrarily defined states. The data-driven algorithm aims to identify these features without the bias of human “chemical intuition”. We demonstrate the method by analyzing the proton exchange reactions in formic acid solvated in small water clusters. The simulations were performed with ab initio MD combined with a method to efficiently sample the rare event, path sampling. Our ML analysis identified relevant geometric variables involved in the proton transfer reaction and how they may change as the number of solvating water molecules changes.
Highlights
In regions far from urban areas, formic acid (FA) has been recognized as one of the main factors which reduces the pH of rainwater, causing acid rain.[1]
The rate of proton transfer from FA to the water molecules has been computed via replica exchange transition interface sampling (RETIS) simulation and ab initio molecular dynamics (MD) simulations
We here investigate the mechanism of reactions via decision trees (DTs) to identify the feature(s) that better correlate for each case with pathways that lead to proton transfer
Summary
In regions far from urban areas, formic acid (FA) has been recognized as one of the main factors which reduces the pH of rainwater, causing acid rain.[1]. A related area with significant theoretical and computational contributions in the last decade is the study of acid ionization in bulk water[14−18] or at the water−air interface.[9,19−23] By contrast, there are only a few papers which focus on the nature of acidic proton transport in small water clusters.[24−30] In these small systems, thermodynamic approaches appropriate for the bulk system are no longer valid. The use of a generalizable approach such as the one we present in this study should be of considerable interest
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