Machine learning-assisted search for MOF-based catalysis in energy and environmental applications

Abstract

Metal-organic frameworks (MOFs) have attracted significant attention in catalysis due to their high surface area, tunable porosity, and flexible structures. These properties make MOFs promising candidates for catalytic reactions including hydrogen evolution, oxygen evolution, carbon dioxide reduction, and organic transformations. In parallel, advances in artificial intelligence (AI) and machine learning (ML) are increasingly influencing catalytic research by accelerating the discovery and optimization of MOF-based catalysts. This review discusses the development of MOF catalysts and their unique structural and electronic features, followed by an overview of ML applications in materials science. It outlines common ML workflows, available databases, widely used algorithms, and software tools employed in catalyst prediction and screening. While previous reviews have highlighted the growing integration of ML and MOF research, this article provides a catalysis-oriented perspective by critically examining the descriptors, datasets, and ML workflows for discovery and optimization of MOF-based catalysts. Recent progress demonstrating the role of ML in accelerating catalyst discovery and performance prediction is highlighted, along with current challenges and limitations. Emerging trends in AI-driven catalyst design and future research directions are also discussed. Overall, this review emphasizes the considerable potential of AI and ML in advancing MOF-based catalytic applications.

Graphical abstract

Keywords

metal-organic framework / machine learning / electrocatalysis / energy storage and CO₂ capture

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