Prospects for Using Artificial Intelligence to Understand Intrinsic Kinetics of Heterogeneous Catalytic Reactions

TL;DR

This paper explores how integrating AI with multiscale models and experiments can revolutionize understanding of heterogeneous catalytic reactions by enabling rapid, interpretable, and automated analysis of complex kinetic systems.

Contribution

It introduces the concept of 'self-driving models' that automate model generation, uncertainty quantification, and coupling of theory with experiments in catalysis research.

Findings

AI accelerates simulations and materials discovery in catalysis.

Generative AI automates model creation and uncertainty quantification.

Integration of AI with experiments offers new insights into catalytic mechanisms.

Abstract

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.