Machine Learning Accelerated Screening Advanced Single‐Atom Anchored MXenes Electrocatalyst for Hydrogen Evolution Reaction

Abstract MXene‐supported single‐atom catalyst (MXene‐SACs) systems are considered promising potential electrocatalysts for the hydrogen evolution reaction (HER) due to their excellent conductivity, stability, and hydrophilicity. However, the complex composition of MXene‐SACs and the unclear structure‐activity relationship limit the rational design of efficient HER catalysts. To address this challenge, a high‐throughput screening strategy that integrates theoretical calculations, machine learning (ML), and experimental validation to efficiently identify MXene‐SACs with outstanding HER performance is developed. Using a database constructed from theoretical calculations as input, a ML model to screen a batch of potential high‐efficiency HER catalysts is built. Based on the distribution pattern of hydrogen evolution barriers predicted by ML and electronic structure analysis, a novel structural descriptor Φ , which can be easily calculated using corresponding properties from the periodic table is derived. The descriptor provides insights into the underlying HER mechanism of MXene‐SACs, where electron transfer from surrounding coordinating atoms to the single atom effectively shifts the d ‐band center to an optimal level (≈ −2.7 eV), minimizing the hydrogen evolution barrier. Guided by this descriptor, Cr 2 CO 2 ‐Pt is synthesized, which exhibits outstanding HER performance, achieving a current density of 1 A cm −2 at an overpotential of 150.7 mV and maintaining long‐term stability over 130 h.