Journal
ACS NANO
Volume 15, Issue 6, Pages 9649-9657Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsnano.0c10851
Keywords
Kagome-lattice Weyl semimetal; Bayesian optimization; Ising model; Kawasaki dynamics; Monte Carlo simulations; Gaussian processes
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Funding
- U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division
- Gordon and Betty Moore Foundation's EPiQS Initiative [GBMF9069]
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The study developed a workflow based on machine learning for analyzing scanning tunneling microscopy images to reconstruct atomic and adatom positions, and using Bayesian optimization to minimize statistical distance between physical models and experimental observations. By optimizing parameters of Ising models and comparing predicted morphologies with experimentally observed surfaces, the workflow can help reconstruct thermodynamic models and associated uncertainties from materials microstructures.
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 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.
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