The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: 16(9), P. 2357 - 2368

Published: Feb. 26, 2025

Accurately controlling the interactions and dynamic changes between multiple active sites (e.g., metals, vacancies, lone pairs of heteroatoms) to achieve efficient catalytic performance is a key issue challenge in design complex reactions involving 2D metal-supported catalysts, metal-zeolites, metal–organic metalloenzymes. With aid machine learning (ML), descriptors play central role optimizing electrochemical elucidating essence activity, predicting more thereby avoiding time-consuming trial-and-error processes. Three kinds descriptors─active center descriptors, interfacial reaction pathway descriptors─are crucial for understanding designing catalysts. Specifically, as sites, synergize with metals significantly promote reduction energy-relevant small molecules. By combining some physical interpretable can be constructed evaluate performance. Future development ML models faces constructing vacancies multicatalysis systems rationally selectivity, stability Utilization generative artificial intelligence multimodal automatically extract would accelerate exploration mechanisms. The transferable from catalysts metalloenzymes provide innovative solutions energy conversion environmental protection.

Language: Английский