Du Junmei, Yan Yifan, Li Xiumei, Chen Jiao, Guo Chunsheng, Chen Yuanzheng, Wang Hongyan
School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University Chengdu Sichuan 610031 China
Chem Sci. 2025 Apr 24. doi: 10.1039/d4sc08725a.
Selecting effective catalysts for the hydrogen evolution reaction (HER) among MXenes remains a complex challenge. While machine learning (ML) paired with density functional theory (DFT) can streamline this search, issues with training data quality, model accuracy, and descriptor selection limit its effectiveness. These hurdles often arise from an incomplete understanding of the catalytic mechanisms. Here, we introduce a mechanism-guided descriptor () for the HER, designed to enhance catalyst screening among ordered transition metal carbide MXenes. This descriptor integrates structural and energetic characteristics, derived from an in-depth analysis of orbital interactions and the relationship between Gibbs free energy of hydrogen adsorption (Δ ) and structural features. The proposed model (Δ = -0.49 - 2.18) not only clarifies structure-activity links but also supports efficient, resource-effective identification of promising catalysts. Our approach offers a new framework for developing descriptors and advancing catalyst screening.
在MXenes中为析氢反应（HER）选择有效的催化剂仍然是一项复杂的挑战。虽然机器学习（ML）与密度泛函理论（DFT）相结合可以简化这一筛选过程，但训练数据质量、模型准确性和描述符选择等问题限制了其有效性。这些障碍通常源于对催化机制的不完全理解。在此，我们引入了一种用于HER的机制引导描述符（），旨在加强对有序过渡金属碳化物MXenes催化剂的筛选。该描述符整合了结构和能量特征，这些特征来自于对轨道相互作用以及氢吸附吉布斯自由能（Δ ）与结构特征之间关系的深入分析。所提出的模型（Δ = -0.49 - 2.18）不仅阐明了结构-活性联系，还支持高效、资源有效的有前景催化剂识别。我们的方法为开发描述符和推进催化剂筛选提供了一个新框架。
