Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires ab initio molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a training dataset size of approximately 31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. These tools are then used to automatically predict a reaction mechanism that is in agreement with the experimentally-reported product distribution. In addition, thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a very minor role in the reaction. Furthermore, a transfer-learning strategy effectively allowed to upgrade the potential energy surface to higher levels of theory (def2-TZVPD basis set and double hybrid functional) using less than 10% additional calculations. Since these methods capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for entropic intermediates. This overall approach is broadly applicable and opens the door to the study of dynamical effects in larger, previously-intractable reactive systems.