Dissertation Proposal: Predicting Organic Thin-Film Structures

Calcinelli, F. (Speaker)

Activity: Talk or presentationPoster presentationScience to science

Description

The properties of a material depend on its structure, which in the case of organic thin films can differ significantly from the configurations that are most favorable in a bulk. A theoretical prediction of the most stable thin film structures through traditional, exhaustive first-principle studies is unfeasible due to the combinatorial explosion in the number of possible polymorphs.

The SAMPLE approach [1] can circumvent this problem, by using the results of a few hundred DFT calculations to provide a reliable evaluation of millions of possible polymorphs through a Bayesian Linear Regression algorithm. It is our intention to extend the application of SAMPLE from organic monolayers to the prediction of the most stable configurations of thin films.

As first step, we predict the best monolayers of a simple organic molecule on graphene, to verify SAMPLE’s effectiveness in describing adsorption on organic substrates. Subsequently, we study thin film structures of pentacenequinone or -tetrone, on which extensive experimental studies have been conducted. On this basis we aim to develop a valid representation for intermolecular interactions in three dimensions and improve our methodologies for local optimization. With this functionality SAMPLE will provide precious insight into the packing geometries of thin films and into the forces that drive their formation.
Period4 Feb 2020
Event titleNAWI Physics Doctoral Seminar-February 2020: null
Event typeSeminar
LocationGraz, Austria

Keywords

  • Structure Prediction
  • Machine Learning
  • thin film
  • hybrid organic/inorganic interface
  • Surface science