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This review aims to explore the integration of explainable artificial intelligence in catalysis to enhance understanding and design of catalysts.

August 7, 2026Academia Catalysis

Explainable artificial intelligence shines a light on catalysis: methods, applications, and future directions

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Review shows how explainable AI enhances understanding of catalysis, suggesting better catalyst design strategies.

This review aims to explore the integration of explainable artificial intelligence in catalysis to enhance understanding and design of catalysts.
Summarizes recent advances in explainable AI for catalysis.
Covers methodologies, evaluation criteria, and applications in heterogeneous, homogeneous, and enzymatic catalysis.
Identifies key electronic and structural descriptors related to catalytic activity.
XAI connects model predictions to interpretable chemical descriptors and reaction features.
Transforms black-box predictions into actionable insights for mechanism-informed catalyst design.
Highlights importance of understanding reaction pathways and dynamics for improved catalytic performance.

Geng et al. (2026) studied this question.

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