Analyzing Complex Reaction Mechanisms Using Path Sampling.

Abstract

We introduce an approach to analyze collective variables (CVs) regarding their predictive power for a reaction. The method is based on already available path sampling data produced by, for instance, transition interface sampling or forward flux sampling, which are path sampling methods used for efficient computation of reaction rates. By a search in CV space, a measure of predictiveness can be optimized and, in addition, the number of CVs can be reduced using projection operations which keep this measure invariant. The approach allows testing hypotheses on the reaction mechanism but could, in principle, also be used to construct the phase-space committor surfaces without the need of additional trajectory sampling. The procedure is illustrated for a one-dimensional double-well potential, a theoretical model for an ion-transfer reaction in which the solvent structure can lower the barrier, and an ab initio molecular dynamics study of water auto-ionization. The analysis technique enhances the quantitative interpretation of path sampling data which can provide clues on how chemical reactions can be steered in desired directions.