  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.
