Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters,ACS Combinatorial Science

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Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters
ACS Combinatorial Science Pub Date : 2020-11-04 , DOI: 10.1021/acscombsci.0c00102
Marc O J Jäger ₁ , Yashasvi S Ranawat ₁ , Filippo Federici Canova _{1,

2} , Eiaki V Morooka ₁ , Adam S Foster _{1,

3}

Affiliation

Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for computational screening methods and requires the development of new techniques for efficient exploration. We present an automated workflow that systematically manages simulations from the generation of nanoclusters through the submission of production jobs, to the prediction of adsorption energies. The presented workflow was designed to screen nanoclusters of arbitrary shapes and size, but in this work the search was restricted to bimetallic icosahedral clusters and the adsorption was exemplified on the hydrogen evolution reaction. We demonstrate the efficient exploration of nanocluster configurations and screening of adsorption energies with the aid of machine learning. The results show that the maximum of the d-band Hilbert-transform ϵ_u is correlated strongly with adsorption energies and could be a useful screening property accessible at the nanocluster level.

中文翻译：

双金属纳米团簇上氢吸附的高效机器学习辅助筛选

纳米团簇增加了一个额外的维度，可以在其中寻找有希望的催化剂候选者，因为材料的催化活性通常在纳米尺度上发生变化。然而，相关原子位点的大搜索空间加剧了计算筛选方法的挑战，需要开发新技术以进行有效探索。我们提出了一个自动化的工作流程，从纳米团簇的生成到生产作业的提交，再到吸附能的预测，系统地管理模拟。所提出的工作流程旨在筛选任意形状和尺寸的纳米团簇，但在这项工作中，搜索仅限于双金属二十面体团簇，并且吸附以析氢反应为例。我们展示了在机器学习的帮助下对纳米团簇配置的有效探索和吸附能的筛选。结果表明，最大d 带希尔伯特变换 ϵ _u与吸附能密切相关，可能是纳米团簇级别的有用筛选特性。

更新日期：2020-12-14

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