Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

2026-10-01

AI Summary

This study addresses critical bottlenecks in artificial intelligence models for chemical engineering, including prediction inaccuracies, data scarcity, and limited interpretability, by exploring the deep integration of AI with first-principles modeling. Methodologically, this work proposes a paradigm shift from purely black-box approaches to physics-informed hybrid frameworks, synergizing atomistic simulations, process systems engineering, and physics-informed neural networks to ensure strict adherence to thermodynamic consistency and physical conservation laws. Consequently, the project significantly enhances predictive robustness and reliability while enabling efficient human–machine collaboration. By safeguarding engineering safety, this research amplifies the scientific value of core chemical engineering principles.

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.

Problem

• Artificial Intelligence
• Machine Learning
• Chemical Engineering
• Predictive Accuracy
• Data Scarcity

Innovation

• Physics-informed frameworks
• Hybrid models
• First-principles integration
• Machine learning
• Chemical engineering