From Atomic Motif to Realistic Single Atom Catalysts through Machine Learning Interatomic Potentials

作者：Seokhyun Choung, Miyeon Kim, Jinuk Moon, Jeong Woo Han · 发表于：ACS Energy Letters · 年份：2025 · DOI：10.1021/acsenergylett.5c03288 · 被引用次数：5 · 研究领域：Machine Learning in Materials Science、Electrocatalysts for Energy Conversion、CO2 Reduction Techniques and Catalysts

High Resolution Image Download MS PowerPoint Slide Metal–nitrogen–carbon (M-N-C) catalysts demonstrate exceptional electrochemical performance, with density functional theory (DFT) simulations successfully guiding atomic-scale optimization of coordination environments. However, recent experiments reveal that catalyst performance depends on phenomena beyond DFT’s spatiotemporal limits. This Perspective examines how machine learning interatomic potentials (MLIPs) bridge this critical gap, achieving orders-of-magnitude acceleration while maintaining near-DFT accuracy. MLIPs capture previously inaccessible phenomena spanning atomic to mesoscopic scales, including structural complexity and electrolyte dynamics. These capabilities reveal how support architecture, collective site interactions, solvation, and reaction kinetics at the mesoscale determine rate-limiting steps in electrochemical reactions. By connecting atomic-level understanding to experimentally relevant scales, MLIPs transform catalyst design from isolated site optimization to comprehensive multiscale engineering.