Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations (Adv. Funct. Mater. 32/2021). (10th August 2021)

• Record Type: Journal Article
• Authors: Järvi, Jari; Alldritt, Benjamin; Krejčí, Ondřej; Todorović, Milica; Liljeroth, Peter; Rinke, Patrick
• Is Part Of: Advanced functional materials. Volume 31:Number 32(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-10
• Subjects: atomic force microscopy -- Bayesian inference -- density‐functional theory -- organic surface adsorbates -- structure search; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202170235
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract : Bayesian Inference In article number 2010853, Patrick Rinke and co‐workers combine artificial intelligence enhanced ab‐initio structure search with atomic force microscopy simulations (SIM) and experiments (EXP) to detect configurations of bulky 3D adsorbates. Bayesian inference is employed to identify distinct stable adsorption configurations of (1S)‐camphor on the Cu(111) surface, followed by SIM‐EXP image feature matching to fingerprint multiple experimental structures.