DescriptionStudying the electronic structure of organic monolayers on inorganic substrates requires knowledge about their atomistic structure. Such monolayers often display rich polymorphism arising from diverse molecular arrangements in different unit cells. The large number of possible arrangements poses a considerable challenge for determining the different polymorphs from first principles.
To meet this challenge, we developed SAMPLE, which employs coarse-grained modeling and machine learning to efficiently map the minima of the potential energy surface of commensurate organic adlayers. Requiring only a few hundred DFT calculations of possible polymorphs as input, we use Bayesian linear regression to determine the parameters of a physically motivated energy model. These parameters yield meaningful physical insight and allow predicting adsorption energies for millions of possible polymorphs with high accuracy.
Beyond that, we continuously push the boundaries of surface structure search, with three particularly noteworthy developments. First, we aim to predict the second adlayer pursuing the goal of studying thin film properties. Second, we generalize SAMPLE to investigate incommensurate adlayers, thereby overcoming one of the largest hurdles of investigating interfaces with periodic boundary condition DFT. Finally, we employ feature recognition to reveal hidden relationships between the properties of adlayers.
 Hörmann et al., Comput. Phys. Commun. 244, 143–155, 2019
|Period||28 Sep 2020|
|Event title||Advanced Materials Day 2020|