Lifting the Curse of Dimensionality on Enhanced Sampling of Reaction Networks with Parallel Bias Metadynamics

A common challenge to applying metadynamics to the study of complex systems is selecting the proper collective variables to bias. The advent of generic collective variables, specifically social permutation invariant (SPRINT) coordinates, has helped to address this challenge by reducing the level of a priori knowledge required to just basic chemical fundamentals. However, the efficiency of biasing SPRINT coordinates can be severely handicapped by the high dimensionality of the bias potential. Here, we circumvent this deficiency by biasing SPRINT coordinates using the parallel bias metadynamics framework. We demonstrate the efficacy of this method to efficiently explore a complex system, without any prior knowledge about transition pathways, by applying it to study the decomposition of γ-ketohydroperoxide and generating a comprehensive reaction network of relevant pathways. The reduction in both computational cost and chemical intuition makes this method a promising option for studying complex reacting systems.