Artificial Intelligence- and Machine Learning-Driven Strategies for Catalyst Design and Sustainable Chemical Processes

Abstract

The integration of artificial intelligence (AI), machine learning (ML), and computational modeling with experimental catalysis is reshaping materials design and chemical process development. Tailored heterogeneous catalysts including supported metals, zeolites, defect-engineered materials, and multi-element systems exhibit enhanced activity, selectivity, and stability through engineered active sites and porosity. AI and ML approaches enable predictive modeling, high-throughput screening, mechanistic insight, and rational catalyst design by linking synthesis conditions, structural features, and performance metrics across scales. Applications span CO2 conversion, methane reforming, hydrogen production, polymer recycling, and photocatalysis, with platforms such as PHOTOREAC, QMOF, and PhotoCatDB facilitating the translation from laboratory experiments to reactor-scale processes. Hybrid strategies that combine mechanistic understanding with data-driven models improve interpretability, predictive accuracy, and process optimization. These advances underscore a paradigm shift toward data-driven catalysis, accelerating discovery, supporting sustainable chemical technologies, and emphasizing the role of human expertise in guiding responsible AI deployment.