Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering
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 Perspective provides an account of 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 life cycle 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
- Ilias Mitrai (ORCID: https://orcid.org/0000-0002-8989-6864)
- Matthew P. Rivera (ORCID: https://orcid.org/0000-0003-2746-7009)
- Ankur Kumar (ORCID: https://orcid.org/0000-0002-2838-4080)
- Andrew James Medford (ORCID: https://orcid.org/0000-0001-8311-9581)
- Joel A. Paulson (ORCID: https://orcid.org/0000-0002-1518-7985)
- Lev Sarkisov (ORCID: https://orcid.org/0000-0001-7637-7670)
- Fèlix Llovell (ORCID: https://orcid.org/0000-0001-7109-6810)
- Linda J. Broadbelt (ORCID: https://orcid.org/0000-0003-4253-592X)
- Ching-Mei Wen (ORCID: https://orcid.org/0000-0002-0790-9239)
- Kirti Chandra Sahu (ORCID: https://orcid.org/0000-0002-7357-1141)
- Calvin Tsay (ORCID: https://orcid.org/0000-0003-2848-2809)
- Junyi Qiao (ORCID: https://orcid.org/0000-0003-1847-8742)
- Marianthi Ierapetritou (ORCID: https://orcid.org/0000-0002-1758-9777)
- Zachary P. Smith (ORCID: https://orcid.org/0000-0002-9630-5890)
- Thomas Alan Kwan (ORCID: https://orcid.org/0000-0002-9153-9962)
- Víctor M. Zavala (ORCID: https://orcid.org/0000-0002-5744-7378)
- Michael Bâldea (ORCID: https://orcid.org/0000-0001-6400-0315)
- Huacheng Zhang (ORCID: https://orcid.org/0000-0001-5464-2947)
- Dan Zhao (ORCID: https://orcid.org/0000-0002-4427-2150)
- Akhilesh Jain
Institutions
- Boston University (US)
- Linde (United States) (US)
- Northwestern University (US)
- Schneider Electric (France) (FR)
- Georgia Institute of Technology (US)
- National University of Singapore (SG)
- Baker Hughes (United States) (US)
- University of Manchester (GB)
- Imperial College London (GB)
- Universitat Rovira i Virgili (ES)
- Monash University (AU)
- Massachusetts Institute of Technology (US)
- Indian Institute of Technology Hyderabad (IN)
- University of Delaware (US)
- The University of Texas at Austin (US)
Publication Details
- Journal
- Industrial & Engineering Chemistry Research
- Published
- 2026-09-30
- DOI
- https://doi.org/10.1021/acs.iecr.6c01806
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00