Machine Learning‐Assisted Active Center Exploration in Atomically Thin MoSxTe2‐x Electrocatalysts for Efficient Hydrogen Evolution

Abstract Modulating the local configurations is widely considered an efficient strategy to promote the catalytic performance of 2D molybdenum disulfide (MoS 2 ) for hydrogen evolution reaction (HER). Although transmission electron microscopy prevails as a central tool to visualize catalysts at atomic resolution, there still lacks a rapid and accurate approach to finding the active centers in the micrographs containing abundant structural information. Herein, a defective MoS x Te 2‐x alloy catalyst is created through low‐temperature sulfurization of 1T′‐MoTe 2 (S‐MoTe 2 ), whose atomic structure is automatically explored using an unsupervised machine learning (ML) framework based on the Zernike feature and uniform manifold approximation and projection (UMAP)‐assisted clustering, enabling the discovery of a novel defect configuration referred antisite Te adatom (Te ads‐Mo ). Density functional theory (DFT) calculations reveal a synergistic enhancement in both the hydrogen adsorption capability and electronic conductivity of these antisite defects, which is experimentally verified by the half‐reduced overpotential and Tafel slope of S‐MoTe 2 alloy compared to its counterparts without Te ads‐Mo . This work provides an intelligent approach to facilitate active center exploration in micrographs and achieves a closed‐loop verification for the ML‐assisted defect discovery via theoretical calculations and electrochemical experiments, displaying how ML and researchers seamlessly cooperate in a scientific workflow for advanced catalyst development.