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

Lukas Hörmann, Andreas Jeindl, Alexander T. Egger, Michael Scherbela, Oliver T. Hofmann*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)143-155
JournalComputer Physics Communications
Publication statusPublished - Nov 2019

Fields of Expertise

  • Advanced Materials Science


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