As a zero-carbon fuel, hydrogen can be produced via electrochemical water splitting using clean electric energy by the hydrogen evolution reaction (HER) process. The ultimate goal of HER catalyst is to replace the expensive Pt metal benchmark with a cheap one with equivalent activities. In this work, we investigated the possibility of HER process on single-atom catalysts (SACs) doped on two-dimensional (2D) GaPS₄ materials, which have a large intrinsic band gap that can be regulated by doping and tensile strain. Based on the machine learning regression analysis, we can expand the prediction of HER performance to more catalysts without expensive DFT calculation. The electron affinity and first ionization energy are the two most important descriptors related to the HER behavior. Furthermore, constrain molecular dynamics with solvation models and constant potentials were applied to understand the dynamics barrier of HER process of Pt SAC on GaPS4 materials. These findings not only provide important insights into the catalytic properties of single-atom catalysts on GaPS₄ 2D materials, but also provides theoretical guidance paradigm for exploration of new catalysts.

作为一种零碳燃料，氢气可以通过析氢反应 (HER) 过程使用清洁电能通过电化学水分解产生。HER催化剂的最终目标是用具有同等活性的廉价铂金属基准替代昂贵的铂金属基准。在这项工作中，我们研究了在二维 (2D) GaPS _{4上掺杂的单原子催化剂 (SAC) 上进行 HER 过程的可能性}材料具有较大的本征带隙，可以通过掺杂和拉伸应变进行调节。基于机器学习回归分析，我们可以将 HER 性能的预测扩展到更多催化剂，而无需昂贵的 DFT 计算。电子亲和力和第一电离能是与 HER 行为相关的两个最重要的描述符。此外，应用具有溶剂化模型和恒定电位的约束分子动力学来了解 Pt SAC 在 GaPS4 材料上的 HER 过程的动力学势垒。这些发现不仅为单原子催化剂对GaPS ₄二维材料的催化性能提供了重要见解，也为探索新型催化剂提供了理论指导范式。