arXiv Open Access 2025

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

Andrew J. Medford Todd N. Whittaker Bjarne Kreitz David W. Flaherty John R. Kitchin

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Abstrak

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.

Topik & Kata Kunci

physics.chem-ph cs.AI

Penulis (5)

Andrew J. Medford

Todd N. Whittaker

Bjarne Kreitz

David W. Flaherty

John R. Kitchin

Format Sitasi

APA MLA BibTeX

Medford, A.J., Whittaker, T.N., Kreitz, B., Flaherty, D.W., Kitchin, J.R. (2025). Prospects for Using Artificial Intelligence to Understand Intrinsic Kinetics of Heterogeneous Catalytic Reactions. https://arxiv.org/abs/2510.18911

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓