Active Learning Accelerates Ab Initio Molecular Dynamics On Reactive Energy Surfaces

Modeling dynamical effects in chemical reactions typically requires ab initio 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 ab initio 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.