Intelligent catalysis: on-demand dynamic self-optimization via responsive feedback
Abstract
responsive, built-in catalytic-related feedback processes, including adaptive dynamic reconfiguration, signal transmission, self-regeneration and reaction pathway modulation, resulting in autonomously enhanced and diversified structure-function properties. Thus, this review systematically establishes the concept of intelligent catalysis, proposes the current intelligent catalysis classification of various self-optimized intelligent effects and outlines future truly intelligent systems with multimodal synergy. The intelligent catalytic mechanisms, design strategies and applications of intelligent catalysts, as well as challenges and countermeasures, are highlighted. We envision that this review can guide the design of next-generation catalysts and facilitate broader intelligent multi-scenario applications.
Fields of Study & Subjects
Machine Learning in Materials Science
Asymmetric Hydrogenation and Catalysis
Neural Networks and Reservoir Computing
Publication details
- Journal / source
- Chemical Society Reviews
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 55
- Issue
- 12
- Pages
- 6571–6590
- Publication date
- 1 January 2026
- Language
- EN