Efficient Exploration of Chemical Kinetics

TL;DR

This paper introduces a comprehensive framework combining physical modeling, statistical surrogates, and advanced algorithms to enable efficient large-scale chemical kinetics simulations, overcoming previous computational limitations.

Contribution

It presents the OT-GP framework for chemically relevant surrogate modeling, along with software and reinforcement learning methods, advancing the state-of-the-art in chemical kinetics exploration.

Findings

State-of-the-art performance in large-scale benchmarks

Robust, chemically relevant potential energy surface surrogates

Effective exploration of complex chemical reaction networks

Abstract

Estimating reaction rates and chemical stability is fundamental, yet efficient methods for large-scale simulations remain out of reach despite advances in modeling and exascale computing. Direct simulation is limited by short timescales; machine-learned potentials require large data sets and struggle with transition state regions essential for reaction rates. Reaction network exploration with sufficient accuracy is hampered by the computational cost of electronic structure calculations, and even simplifications like harmonic transition state theory rely on prohibitively expensive saddle point searches. Surrogate model-based acceleration has been promising but hampered by overhead and numerical instability. This dissertation presents a holistic solution, co-designing physical representations, statistical models, and systems architecture in the Optimal Transport Gaussian Process (OT-GP) framework. Using physics-aware optimal transport metrics, OT-GP creates compact, chemically relevant surrogates of the potential energy surface, underpinned by statistically robust sampling. Alongside EON software rewrites for long timescale simulations, we introduce reinforcement learning approaches for both minimum-mode following (when the final state is unknown) and nudged elastic band methods (when endpoints are specified). Collectively, these advances establish a representation-first, modular approach to chemical kinetics simulation. Large-scale benchmarks and Bayesian hierarchical validation demonstrate state-of-the-art performance and practical exploration of chemical kinetics, transforming a longstanding theoretical promise into a working engine for discovery.