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We propose a supervised machine learning algorithm, decision trees, toanalyze molecular dynamics output. The approach aims to identify thepredominant geometric features which correlate with trajectories thattransition between two arbitrarily defined states. The data-based algorithmaims to identify such features in an approach which is unbiased by human"chemical intuition". We demonstrate the method by analyzing proton exchangereactions in formic acid (FA) solvated in small water clusters. The simulationswere performed with ab initio molecular dynamics combined with a method forgenerating rare events, specifically path sampling. Our machine learninganalysis identified mechanistic descriptions of the proton transfer reactionfor the different water clusters.

Chemistrees: data driven identification of reaction pathways via machine learning