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通过多级高通量计算和机器学习识别用于析氢的金属过渡金属二硫属化物

Identifying Metallic Transition-Metal Dichalcogenides for Hydrogen Evolution through Multilevel High-Throughput Calculations and Machine Learning.

作者信息

Ran Nian, Sun Bo, Qiu Wujie, Song Erhong, Chen Tingwei, Liu Jianjun

机构信息

State Key Laboratory of High Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 200050, China.

Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

J Phys Chem Lett. 2021 Mar 4;12(8):2102-2111. doi: 10.1021/acs.jpclett.0c03839. Epub 2021 Feb 24.

DOI:10.1021/acs.jpclett.0c03839
PMID:33625239
Abstract

High-performance electrocatalysts not only exhibit high catalytic activity but also have sufficient thermodynamic stability and electronic conductivity. Although metallic 1T-phase MoS and WS have been successfully identified to have high activity for hydrogen evolution reaction, designing more extensive metallic transition-metal dichalcogenides (TMDs) faces a large challenge because of the lack of a full understanding of electronic and composition attributes related to catalytic activity. In this work, we carried out systematic high-throughput calculation screening for all possible existing two-dimensional TMD (2D-TMD) materials to obtain high-performance hydrogen evolution reaction (HER) electrocatalysts by using a few important criteria, such as zero band gap, highest thermodynamic stability among available phases, low vacancy formation energy, and approximately zero hydrogen adsorption energy. A series of materials-perfect monolayer VS and NiS, transition-metal ion vacancy (TM-vacancy) ZrTe and PdTe, chalcogenide ion vacancy (X-vacancy) MnS, CrSe, TiTe, and VSe-have been identified to have catalytic activity comparable with that of Pt(111). More importantly, electronic structural analysis indicates active electrons induced by defects are mostly delocalized in the nearest-neighbor and next-nearest neighbor range, rather than a single-atom active site. Combined with the machine learning method, the HER-catalytic activity of metallic phase 2D-TMD materials can be described quantitatively with local electronegativity (0.195·LEf + 0.205·LEs) and valence electron number (Vtmx), where the descriptor is Δ = 0.093 - (0.195·LEf + 0.205·LEs) - 0.15·Vtmx.

摘要

高性能电催化剂不仅具有高催化活性,还具备足够的热力学稳定性和电子导电性。尽管已成功确认金属1T相的MoS和WS对析氢反应具有高活性,但由于对与催化活性相关的电子和组成属性缺乏全面了解,设计更多种类的金属过渡金属二硫属化物(TMD)面临巨大挑战。在这项工作中,我们对所有可能存在的二维TMD(2D-TMD)材料进行了系统的高通量计算筛选,以通过一些重要标准获得高性能析氢反应(HER)电催化剂,这些标准包括零带隙、可用相中最高的热力学稳定性、低空位形成能以及近似零的氢吸附能。已确定一系列材料——完美的单层VS和NiS、过渡金属离子空位(TM-空位)ZrTe和PdTe、硫属化物离子空位(X-空位)MnS、CrSe、TiTe和VSe——具有与Pt(111)相当的催化活性。更重要的是,电子结构分析表明,由缺陷诱导的活性电子大多在最近邻和次近邻范围内离域,而非单个原子活性位点。结合机器学习方法,金属相二维TMD材料的HER催化活性可用局部电负性(0.195·LEf + 0.205·LEs)和价电子数(Vtmx)进行定量描述,其中描述符为Δ = 0.093 - (0.195·LEf + 0.205·LEs) - 0.15·Vtmx。

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