Chemical engineers rely on models for design,research,and daily decision-making,often with potentially large financial and safety implications.Previous efforts a few decades ago to combine artificial intelli-gence and chemical engineering for modeling were unable to fulfill the expectations.In the last five years,the increasing availability of data and computational resources has led to a resurgence in machine learning-based research.Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases,benchmarks,and representations for chemical appli-cations and new machine learning frameworks.Machine learning has significant advantages over tradi-tional modeling techniques,including flexibility,accuracy,and execution speed.These strengths also come with weaknesses,such as the lack of interpretability of these black-box models.The greatest oppor-tunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns,learn from data,and become more intelligent over time.The greatest threat in artificial intelli-gence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis.Nevertheless,machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.