Discover, Sample, and Refine: Exploring Chemistry with Enhanced Sampling Techniques

Over the last few decades, enhanced sampling methods have been continuously improved. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and determination of reaction paths. In a three-step strategy, at first we use a collective variable derived from spectral graph theory in conjunction with the explore variant of the on-the -fly probability enhanced sampling method to drive reaction discovery runs. Once different chemical products are determined, we construct an ad-hoc neural network-based collective variable to improve sampling, and finally we refine the results using the free energy perturbation theory and a more accurate Hamiltonian. We apply this strategy to both intramolecular and intermolecular reactions. Our workflow requires minimal user input and extends the power of ab initio molecular dynamics to explore and characterize the reaction space.

Automated summary

This article proposes a modular workflow for blind reaction discovery and determination of reaction paths. The workflow utilizes advanced sampling methods and neural network-based collective variables to improve sampling and refinement of results. The approach can extend the power of ab initio molecular dynamics in exploring and characterizing reaction space.