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

• URL: http://arxiv.org/abs/2510.18911v1
• Date: Tue, 21 Oct 2025 04:35:26 GMT
• Title: Prospects for Using Artificial Intelligence to Understand Intrinsic Kinetics of Heterogeneous Catalytic Reactions
• Authors: Andrew J. Medford, Todd N. Whittaker, Bjarne Kreitz, David W. Flaherty, John R. Kitchin,
• Abstract summary: Key frontier is integrating AI with multiscale models and multimodal experiments.<n> inconsistent data quality and model complexity limit mechanistic discovery.<n>Generative and agentic AI can automate model generation, quantify uncertainty, and couple theory with experiment.
• Score: 2.0971479389679337
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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