Active learning accelerates ab initio molecular dynamics on reactive energy surfaces

Abstract

<h2>Summary</h2> Modeling dynamical effects in chemical reactions typically requires <i>ab initio</i> molecular dynamics (AIMD) simulations due to the breakdown of transition state theory (TST). Reactive AIMD simulations are limited to lower-accuracy electronic structure methods and weak statistics because quantum mechanical energies and forces must be evaluated at femtosecond time resolution over many replicas. We report a data-driven pipeline that allows for the treatment of dynamical effects with the same level of theory and overall cost as that of TST approaches. High-throughput <i>ab initio</i> calculations and autonomous data acquisition are coupled to graph convolutional neural-network interatomic potentials, allowing for inexpensive reactive AIMD simulations at quantum mechanical accuracy. We demonstrate the approach by accurately simulating post-TS dynamical effects in three distinct pericyclic reactions, including a challenging trispericyclic reaction with a complex bifurcating potential energy surface. This approach is broadly applicable to understanding dynamical effects and predicting reaction outcomes in large, previously intractable systems.