Rapid prediction of molecule arrangements on metal surfaces via Bayesian optimization
Rapid prediction of molecule arrangements on metal surfaces via Bayesian optimization
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DOI：
10.7567/apex.10.065502
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发表时间：
2017-06-01
影响因子：
2.3
通讯作者：
Hitosugi, Taro
中科院分区：
文献类型：
--
作者：
Packwood, Daniel M.;Hitosugi, Taro
吸附在金属表面上的分子的空间排列很难通过第一性原理计算和标准优化算法来预测。在这封信中，我们证明了一种称为贝叶斯优化的机器学习技术可以在数十次密度泛函理论能量评估中优化铜（111）表面上两种中等大小的芳香族吸附物的排列。因此，这里报告的方法是朝着化学改性表面的第一原理结构预测迈出的一步，而不需要首先根据实验数据指定分子吸附物的排列。(C)2017日本应用物理学会
The spatial arrangement of molecule adsorbates on a metal surface is very difficult to predict via first-principles calculations and standard optimizing algorithms. In this Letter, we show that a machine learning technique called Bayesian optimization can optimize the arrangement of two medium-sized aromatic adsorbates on a copper (111) surface within tens of density functional theory energy evaluations. The methodology reported here is therefore a step toward first-principles structure predictions for chemically modified surfaces, without the need to first specify the arrangement of molecule adsorbates from experimental data. (C) 2017 The Japan Society of Applied Physics