Learning all-atom molecular reactions using data-driven approaches

Molecular reactivity spans extended length and time scales, making them costly to simulate using accurate density functional theory (DFT) approaches or limited in chemical transferability/scope using reactive force fields. In this work, we describe how simulations of liquid structure and dynamics of organic molecules undergoing thermal decomposition reactions can be achieved using hybrid DFT, active learning [1], and machine learning force fields [2].



Active learning reduces the overall computational cost of hybrid DFT by employing a fast, uncertainty-aware force field to collect only “sufficiently”-uncorrelated DFT frames. In addition, high-temperature configurations with radicals or free gasses, which are potential decomposition products, can be collected on-the-fly. The approach is useful in situations where classical force fields are either unavailable or lacking in expressivity.

When training data from active learning are fed into a data-efficient equivariant neural network, molecular decomposition and reaction pathways can be traced with first-principles, all-atom resolution by identifying reaction pathways to product formation. We apply the approach to study the thermal decomposition of a “green” solvent used in battery recycling and validate our results against experimental characterization.