Machine Learning‐Assisted Active Center Exploration in Atomically Thin MoS x Te 2‐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.