Chemistrees: data driven identification of reaction pathways via machine learning

Abstract

We propose a supervised machine learning algorithm, decision trees, to analyze molecular dynamics output. The approach aims to identify the predominant geometric features which correlate with trajectories that transition between two arbitrarily defined states. The data-based algorithm aims to identify such features in an approach which is unbiased by human "chemical intuition". We demonstrate the method by analyzing proton exchange reactions in formic acid (FA) solvated in small water clusters. The simulations were performed with ab initio molecular dynamics combined with a method for generating rare events, specifically path sampling. Our machine learning analysis identified mechanistic descriptions of the proton transfer reaction for the different water clusters.

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