Machine-learning driven global optimization of surface adsorbate geometries

作     者：Hyunwook Jung Lena Sauerland Sina Stocker Karsten Reuter Johannes T.Margraf 

作者机构：Fritz-Haber-Institut der Max-Planck-GesellschaftFaradayweg 4-6D-14195 BerlinGermany 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2023年第9卷第1期

页      面：1196-1203页

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 070304[理学-物理化学(含∶化学物理)] 08[工学] 0817[工学-化学工程与技术] 0703[理学-化学] 

基　　金：Alexander von Humboldt-Stiftung  AvH 

主　　题：optimization global implies 

摘      要：The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in computational catalysis *** the relatively large reaction intermediates frequently encountered,e.g.,in syngas conversion,a multitude of possible binding motifs leads to complex potential energy surfaces(PES),*** implies that finding the optimal structure is a difficult global optimization problem,which leads to significant uncertainty about the stability of many *** tackle this issue,we present a global optimization protocol for surface adsorbate geometries which trains a surrogate machine learning potential *** approach is applicable to arbitrary surface models and adsorbates and minimizes both human intervention and the number of required DFT calculations by iteratively updating the training set with configurations explored by the *** demonstrate the efficiency of this approach for a diverse set of adsorbates on the Rh(111)and(211)surfaces.