Efficient Exploration of Chemical Kinetics

2025-10-24

AI Summary

Large-scale chemical reaction kinetics simulations have long suffered from limitations in accessible timescales, high computational cost for saddle-point searches, poor generalizability of machine-learned potentials, and low efficiency in reaction network exploration. To address these challenges, we propose a physics-informed Optimal Transport Gaussian Process (OT-GP) framework that integrates optimal transport metrics, Gaussian process surrogate modeling, reinforcement learning–guided path search, minimum-mode following, and the nudged elastic band method—enabling compact potential energy surface representation and efficient long-timescale dynamics simulation. Through systematic refactoring of the EON software platform and Bayesian hierarchical validation, our approach significantly improves reaction pathway discovery and network construction efficiency on large-scale benchmarks. It establishes, for the first time, a “representation-first, modular” paradigm for chemical kinetics simulation, advancing theoretical computation toward a scalable, discovery-oriented engine.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Estimating reaction rates and chemical stability efficiently
• Overcoming computational cost limitations in reaction network exploration
• Addressing accuracy challenges in transition state region modeling

Innovation

• OT-GP framework creates compact surrogates using optimal transport
• Reinforcement learning enhances minimum-mode and elastic band methods
• Modular representation-first approach enables practical kinetics exploration