Structure Prediction for Surface-Induced Phases of Organic Monolayers: Overcoming the Combinatorial Bottleneck

Abstract

Structure determination and prediction pose a major challenge to computational material science, demanding efficient global structure search techniques tailored to identify promising and relevant candidates. A major bottleneck is the fact that due to the many combinatorial possibilities, there are too many possible geometries to be sampled exhaustively. Here, an innovative computational approach to overcome this problem is presented that explores the potential energy landscape of commensurate organic/inorganic interfaces where the orientation and conformation of the molecules in the tightly packed layer is close to a favorable geometry adopted by isolated molecules on the surface. It is specifically designed to sample the energetically lowest lying structures, including the thermodynamic minimum, in order to survey the particularly rich and intricate polymorphism in such systems. The approach combines a systematic discretization of the configuration space, which leads to a huge reduction of the combinatorial possibilities with an efficient exploration of the potential energy surface inspired by the Basin-Hopping method. Interfacing the algorithm with first-principles calculations, the power and efficiency of this approach is demonstrated for the example of the organic molecule TCNE (tetracyanoethylene) on Au(111). For the pristine metal surface, the global minimum structure is found to be at variance with the geometry found by scanning tunneling microscopy. Rather, our results suggest the presence of surface adatoms or vacancies that are not imaged in the experiment.

Bayesian inference of atomistic structure in functional materials

“…2a: once the simulation 'building blocks' are identified, the learning would progress from single adsorbates to molecular aggregates and monolayers. While some methods acquire single adsorption configurations by intuition and focus on complex lattice-based film morphology search 33,34 , we aim to treat both the molecular adsorbates and aggregates within the BOSS framework by increasing search degrees of freedom.…”

Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

“…Traditionally, stable structures have been identified by initializing the minima search with estimated low-energy structures, based on chemical intuition [ 17 – 18 ], thus narrowing down the search space. With hybrid materials, however, this intuition is difficult to apply and can lead to biased or incorrect results.…”

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

“…SAMPLE stands for Surface Adsorbate Polymorph Prediction with Little Effort and allows to efficiently predict polymorphs at organic/inorganic interfaces from first principles. In two previous publications [30,31] we have already successfully applied parts of the SAMPLE approach. Here we present a comprehensive explanation and demonstration.…”