Computational Structure Prediction for Interfaces: What is Currently Possible?

  • Lukas Hörmann (Contributor)
  • Andreas Jeindl (Contributor)
  • Fabio Calcinelli (Contributor)
  • Johannes Cartus (Contributor)
  • Hofmann, O. (Contributor)

Activity: Talk or presentationPoster presentationScience to science


Studying 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 arrangements poses a big challenge for determining the different polymorphs from first principles. To meet this challenge, we developed SAMPLE[1], which employs coarse-grained modeling and machine learning to efficiently map the minima of the potential energy surface of commensurate organic adlayers. With only a few hundred DFT calculations 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 noteworthy developments: i) predicting the second adlayer pursuing the goal of studying thin film properties; ii) generalizing SAMPLE to investigate incommensurate adlayers; iii) employing feature recognition to reveal hidden relationships between the interface properties. [1] Hörmann et al., Comput. Phys. Commun. 244, 143*155, 2019
Period2 Mar 2021
Event titleDPG-Frühjahrstagung (DPG Spring Meeting) of the Surface Science Division
Event typeConference
Degree of RecognitionInternational