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This article examines how artificial intelligence and machine learning are being integrated with first-principles models to enhance chemical engineering, specifically in areas like atomic-scale simulations and catalyst discovery. The authors argue that these tools amplify rather than replace core engineering principles by adopting hybrid frameworks that respect physical laws and thermodynamic consistency. This shift toward physics-informed AI addresses critical challenges in predictive accuracy and interpretability within industrial operations.
Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering
arXiv:2610.02014v1 Announce Type: new Abstract: The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are formulated, analyzed, and solved. Advances in computing, data availability, and learning algorithms have enabled AI/ML methods to impact applications spanning atomic-scale simulations, materials and catalyst discovery, transport and thermodynamics, separations, process systems engineering, and industrial operations. This article provides a perspective on recent methodological developments and representative applications, emphasizing how AI/ML tools are being integrated with first-principles models to address challenges of predictive accuracy, data scarcity, extrapolation, interpretability, and model lifecycle management. Across domains, a unifying trend is the move away from purely black-box approaches toward hybrid and physics-informed frameworks that explicitly respect conservation laws, thermodynamic consistency, and known structural constraints. These approaches not only improve robustness and reliability, but also enable meaningful human-AI collaboration by providing information at an appropriate level of abstraction for the task and decision context. We conclude that AI and ML are not replacing the core principles of chemical engineering; rather, they are amplifying them. As the field advances toward increasingly autonomous, adaptive, and sustainable systems, the thoughtful integration of AI/ML with first-principles understanding and domain expertise will be essential to realizing their full potential across both research and industrial practice.
Authors:Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou, Akhilesh Jain, Ankur Kumar, Thomas A. Kwan, Fèlix Llovell, Andrew J. Medford, Ilias Mitrai, Joel Paulson, Junyi Qiao, Matthew P. Rivera, Kirti C. Sahu, Lev Sarkisov, Zachary P. Smith, Calvin Tsay, Ching-Mei Wen, Victor M. Zavala, Huacheng Zhang, Dan Zhao
Abstract:The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are formulated, analyzed, and solved. Advances in computing, data availability, and learning algorithms have enabled AI/ML methods to impact applications spanning atomic-scale simulations, materials and catalyst discovery, transport and thermodynamics, separations, process systems engineering, and industrial operations. This article provides a perspective on recent methodological developments and representative applications, emphasizing how AI/ML tools are being integrated with first-principles models to address challenges of predictive accuracy, data scarcity, extrapolation, interpretability, and model lifecycle management. Across domains, a unifying trend is the move away from purely black-box approaches toward hybrid and physics-informed frameworks that explicitly respect conservation laws, thermodynamic consistency, and known structural constraints. These approaches not only improve robustness and reliability, but also enable meaningful human-AI collaboration by providing information at an appropriate level of abstraction for the task and decision context. We conclude that AI and ML are not replacing the core principles of chemical engineering; rather, they are amplifying them. As the field advances toward increasingly autonomous, adaptive, and sustainable systems, the thoughtful integration of AI/ML with first-principles understanding and domain expertise will be essential to realizing their full potential across both research and industrial practice.
Artificial Intelligence (cs.AI)
arXiv:2610.02014 [cs.AI]
(or arXiv:2610.02014v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.02014 arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1021/acs.iecr.6c01806 DOI(s) linking to related resources
Full text under the publisher's open licence · 280 words
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