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首页> 外文期刊>The journal of physical chemistry, C. Nanomaterials and interfaces >High-Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXene Materials
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High-Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXene Materials

机译：MxENE材料中氢化反应催化剂的高通量筛选

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摘要

In this study, machine learning (ML) models combined with density functional theory (DFT) calculations and Gibbs free energy of hydrogen adsorption (Delta G(H*)) were employed to facilitate the high-throughput screening of hydrogen evolution reaction (HER) catalysts in various MXene materials. The predicted Delta G(H*) values show a high-level accuracy via the random forest algorithm by using only simple elemental features. A total of 299 MXene materials were screened by DFT calculations and four ML models (Elman Artificial Neural Networks, kernel ridge regression, support vector regression, and random forest regression algorithms). Using the simple elemental information, the random forest algorithm shows a high-level predicted accuracy with a low testing root-mean-square error of 0.27 eV. Os2B- and S-terminated Scn+1Nn (n = 1, 2, 3) were discovered to be the active catalysts as Delta G(H*) approaches zero with wide hydrogen coverages (theta from 1/9 to 4/9). S functional groups play a crucial role in regulating the HER performance due to the antibonding states which are full of electrons. Consequently, it weakens the adsorption of H* which is the key step of HER. In summary, the present work suggests that ML models are competitive tools in accelerating the screening of efficient HER catalysts.
机译：在本研究中，采用机器学习（ML）模型与密度泛函理论（DFT）计算和GIBBS自由能量的氢吸附（Delta G（H *））促进氢进化反应的高通量筛选（她）催化剂在各种蒙薄材料中。预测的ΔG（H *）值通过仅使用简单的元素特征来显示通过随机林算法的高电平精度。通过DFT计算和四毫升模型（Elman人工神经网络，核Ridge回归，支持向量回归和随机林回归算法），共筛选了299毫升的材料。使用简单的元素信息，随机林算法显示了具有0.27eV的低测试根均方误差的高级预测精度。发现OS2B-和S-终止的SCN + 1NN（n = 1,2,3）是活性催化剂，作为ΔG（H *）接近零氢覆盖物（从1/9至4/9的θ）。 S官能团在调节其由于充满电子的抗体状态而在调节她的性能方面发挥至关重要作用。因此，它削弱了H *的吸附，这是她的关键步骤。总之，目前的工作表明，ML模型是加速筛选有效催化剂的竞争工具。

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    Zheng Jingnan; Sun Xiang; Qu Chenglong; Yan Yilong; Yao Zihao; Deng Shengwei; Zhong Xing; Zhuang Guilin; Wei Zhongzhe; Wang Jianguo;

  • 作者单位

    Zhejiang Univ Technol Inst Ind Catalysis State Key Lab Breeding Base Green Chem Synth Tech Coll Chem Engn Hangzhou 310032 Peoples R China;

    Zhejiang Univ Technol Inst Ind Catalysis State Key Lab Breeding Base Green Chem Synth Tech Coll Chem Engn Hangzhou 310032 Peoples R China;

    Zhejiang Univ Technol Inst Ind Catalysis State Key Lab Breeding Base Green Chem Synth Tech Coll Chem Engn Hangzhou 310032 Peoples R China;

    Zhejiang Univ Technol Inst Ind Catalysis State Key Lab Breeding Base Green Chem Synth Tech Coll Chem Engn Hangzhou 310032 Peoples R China;

    Zhejiang Univ Technol Inst Ind Catalysis State Key Lab Breeding Base Green Chem Synth Tech Coll Chem Engn Hangzhou 310032 Peoples R China;

    Zhejiang Univ Technol Inst Ind Catalysis State Key Lab Breeding Base Green Chem Synth Tech Coll Chem Engn Hangzhou 310032 Peoples R China;

    Zhejiang Univ Technol Inst Ind Catalysis State Key Lab Breeding Base Green Chem Synth Tech Coll Chem Engn Hangzhou 310032 Peoples R China;

    Zhejiang Univ Technol Inst Ind Catalysis State Key Lab Breeding Base Green Chem Synth Tech Coll Chem Engn Hangzhou 310032 Peoples R China;

    Zhejiang Univ Technol Inst Ind Catalysis State Key Lab Breeding Base Green Chem Synth Tech Coll Chem Engn Hangzhou 310032 Peoples R China;

    Zhejiang Univ Technol Inst Ind Catalysis State Key Lab Breeding Base Green Chem Synth Tech Coll Chem Engn Hangzhou 310032 Peoples R China;

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  • 正文语种 eng
  • 中图分类 物理化学（理论化学）、化学物理学;
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