Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland.
Nanolayers Research Computing Ltd, 1 Granville Court, Granville Road, London, N12 0HL, England, United Kingdom.
ACS Comb Sci. 2020 Dec 14;22(12):768-781. doi: 10.1021/acscombsci.0c00102. Epub 2020 Nov 4.
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 -band Hilbert-transform ϵ is correlated strongly with adsorption energies and could be a useful screening property accessible at the nanocluster level.
纳米团簇为寻找有前途的催化剂候选物提供了一个额外的维度，因为材料的催化活性通常在纳米尺度上发生变化。然而，相关原子位置的巨大搜索空间加剧了计算筛选方法的挑战，需要开发新的技术来进行有效的探索。我们提出了一种自动化的工作流程，它可以系统地管理从纳米团簇生成到生产作业提交再到吸附能预测的模拟。所提出的工作流程旨在筛选任意形状和大小的纳米团簇，但在这项工作中，搜索仅限于双金属二十面体团簇，吸附能的例子是在析氢反应中。我们借助机器学习来演示纳米团簇构型的有效探索和吸附能的筛选。结果表明，能带 Hilbert 变换 ε 的最大值与吸附能有很强的相关性，因此ε 可能是一个有用的纳米团簇水平上的筛选特性。
