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.
@article{arxiv.1811.11702,
title = {SAMPLE: Surface structure search enabled by coarse graining and statistical learning},
author = {Lukas Hörmann and Andreas Jeindl and Alexander T. Egger and Michael Scherbela and Oliver T. Hofmann},
journal= {arXiv preprint arXiv:1811.11702},
year = {2020}
}