Extracting Catalytic Reaction Mechanisms from Large-Scale Simulations Accelerated by Machine Learning Interatomic Potentials

Machine learning interatomic potentials (MLIPs) have become a prevalent approach to bridging the gap between slow-but-accurate ab initio calculations and fast-but-inaccurate empirical potentials for molecular dynamics. Among MLIPs, there is a pareto front of models with different tradeoffs between accuracy and speed. The FLARE interatomic potential aims to push the boundary of scalability and performance, while maintaining sufficient accuracy to study complex, reactive systems.

In our recent work [1], we demonstrated the ability of FLARE to simulate heterogeneous catalytic reactions in systems of up to 0.5 trillion atoms using 27336 GPUs, with an accurate Bayesian MLIP efficiently trained using active learning. With the exceptional speed of FLARE on modern HPC architectures, we can combine the atomistic resolution of molecular dynamics simulations with the large length and time scales required for realistic simulations and sufficient sampling of rare catalytic events, while providing the near-quantum level of accuracy provided by the MLIP. In this talk, we will showcase how this combination of scales and accuracy enables new insights into chemically complex processes such as heterogeneous catalysis through high-resolution measurements of reaction rates and residence times.

[1] arXiv:2204.12573