Multitask learning of reactive force fields and collective variables to accelerate molecular dynamics and enhanced sampling of rare catalytic events

Direct ab initio molecular dynamics for rare event reaction rate estimation can be prohibitive due to its poor scaling and long simulation time needed to accumulate sufficient statistics. On the other hand, enhanced sampling techniques can accelerate the simulation but require good collective variables, which can be hard to design for complex reactions. We demonstrate a data-driven method to address these two problems using machine-learned force fields and collective variables.

This work uses a multitask learning framework[1] based on Neural Equivariant Interatomic Potentials (NequIP)[2] to train force fields with quantum chemical accuracy and discover critical collective variables for highly efficient free energy landscape exploration. Short molecular dynamics simulations around the transition states and basins are used to optimize the networks. The trained force fields can then be used to predict forces and energies. At the same time, the trained latent space is then used as the reaction coordinate for enhanced sampling to obtain free energy barriers of reactions. This learning framework is demonstrated on estimating the reaction free energy of formate dehydrogenation on a Cu(110) surface.