Summary
Chemical engineering problems, from understanding tiny atoms to running large factories, are becoming easier to solve using artificial intelligence (AI) and machine learning (ML). The authors show how combining AI with traditional scientific methods helps overcome challenges like limited data and difficult-to-explain predictions. Instead of relying only on 'black box' AI, they discuss hybrid methods that respect the physical laws chemical engineers know well. This approach makes AI more trustworthy and easier for humans to work with, ultimately supporting better and more sustainable chemical processes.
What this means in practice
- •For chemical plant operators: Improve industrial process control by integrating AI methods with physical models to enhance prediction accuracy and operational reliability.
- •For materials developers: Accelerate discovery of new materials and catalysts by combining machine learning with atomic-scale simulations respecting physical laws.
A position paper. It proposes an approach and reports no results.
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.