SAMPLE: Surface structure search enabled by coarse graining and statistical learning

被引:27
|
作者
Hoermann, Lukas [1 ]
Jeindl, Andreas [1 ]
Egger, Alexander T. [1 ]
Scherbela, Michael [1 ]
Hofmann, Oliver T. [1 ]
机构
[1] Graz Univ Technol, Inst Solid State Phys, NAWI Graz, Petersgasse 16, A-8010 Graz, Austria
基金
奥地利科学基金会;
关键词
Hybrid organic/inorganic interface; Bayesian linear regression; Polymorphism; Surface induced phase; First principles simulation; Naphthalene on Cu(111); CRYSTAL-STRUCTURE; MECHANICAL-PROPERTIES; GLOBAL OPTIMIZATION; NAPHTHALENE; POLYMORPHISM; PREDICTION; CLUSTERS;
D O I
10.1016/j.cpc.2019.06.010
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this publication we introduce SAMPLE, a structure search approach for commensurate organic monolayers on inorganic substrates. Such monolayers often show rich polymorphism with diverse molecular arrangements in differently shaped unit cells. Determining the different commensurate polymorphs from first principles poses a major challenge due to the large number of possible molecular arrangements. To meet this challenge, SAMPLE employs coarse-grained modeling in combination with Bayesian linear regression to efficiently map the minima of the potential energy surface. In addition, it uses ab initio thermodynamics to generate phase diagrams. Using the example of naphthalene on Cu(111), we comprehensively explain the SAMPLE approach and demonstrate its capabilities by comparing the predicted with the experimentally observed polymorphs. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:143 / 155
页数:13
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