Prospects for Using Artificial Intelligence to Understand Intrinsic Kinetics of Heterogeneous Catalytic Reactions
This work tackles the 'many-to-one' problem in catalysis research, offering incremental improvements through AI integration for more efficient mechanistic discovery.
The paper addresses the challenge of linking intrinsic kinetics to observables in heterogeneous catalytic reactions by integrating AI with multiscale models and multimodal experiments, proposing that generative and agentic AI can automate model generation to achieve interpretable and transferable understanding.
Artificial intelligence (AI) is influencing heterogeneous catalysis research by accelerating simulations and materials discovery. A key frontier is integrating AI with multiscale models and multimodal experiments to address the "many-to-one" challenge of linking intrinsic kinetics to observables. Advances in machine-learned force fields, microkinetics, and reactor modeling enable rapid exploration of chemical spaces, while operando and transient data provide unprecedented insight. Yet, inconsistent data quality and model complexity limit mechanistic discovery. Generative and agentic AI can automate model generation, quantify uncertainty, and couple theory with experiment, realizing "self-driving models" that produce interpretable, reproducible, and transferable understanding of catalytic systems.