Differentiable Simulations for Enhanced Sampling of Rare Events
This addresses the challenge of enhanced sampling for rare events in computational chemistry, offering a method that eliminates the need for preselected collective variables, though it appears incremental with improvements to existing differentiable simulation techniques.
The paper tackled the problem of simulating rare events in chemical reactions without relying on heuristically chosen collective variables, by proposing differentiable simulations (DiffSim) that merge reaction path discovery and biasing potential estimation into an end-to-end optimization, achieving successful transition path discovery for the Muller-Brown model and alanine dipeptide.
Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically chosen collective variables (CVs). We propose using differentiable simulations (DiffSim) for the discovery and enhanced sampling of chemical transformations without a need to resort to preselected CVs, using only a distance metric. Reaction path discovery and estimation of the biasing potential that enhances the sampling are merged into a single end-to-end problem that is solved by path-integral optimization. This is achieved by introducing multiple improvements over standard DiffSim such as partial backpropagation and graph mini-batching making DiffSim training stable and efficient. The potential of DiffSim is demonstrated in the successful discovery of transition paths for the Muller-Brown model potential as well as a benchmark chemical system - alanine dipeptide.