Bayesian learning of adatom interactions from atomically-resolved imaging data

TL;DR

This paper introduces a machine learning and Bayesian optimization workflow to analyze atomically-resolved imaging data, reconstruct adatom interactions, and predict surface morphologies, enhancing understanding of surface thermodynamics.

Contribution

It develops a universal method combining imaging analysis, generative modeling, and Bayesian optimization to infer adatom interaction parameters from experimental data.

Findings

Successfully reconstructs adatom interaction models from STM images.

Predicts surface morphologies across different adatom concentrations.

Provides a reproducible workflow with available code.

Abstract

Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. Here we develop a workflow based on a machine learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow is universal and can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.