Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations
authors Vlcek, L; Ziatdinov, M; Maksov, A; Tselev, A; Baddorf, AP; Kalinin, SV; Vasudevan, RK
nationality International
journal ACS NANO
author keywords scanning tunneling microscopy; statistical inference; generative model; segregation; manganite; thin film
keywords STATISTICAL DISTANCE; MANGANITES; INFRASTRUCTURE; IDENTIFICATION; SEGREGATION; EXTRACTION; SURFACES; ELECTRON; PHYSICS; SEARCH
abstract In materials characterization, traditionally a single experimental sample is used to derive information about a single point in the composition space, while the imperfections, impurities, and stochastic details of material structure are deemed irrelevant or complicating factors in the analysis. Here we demonstrate that atomic-scale studies of a single nominal composition can provide information about microstructures and thermodynamic response over a finite area of chemical space. Using the principles of statistical inference, we develop a framework for incorporating structural fluctuations into statistical mechanical models and use it to solve the inverse problem of deriving effective interatomic interactions responsible for elemental segregation in a La5/8Ca3/8MnO3 thin film. The results are further analyzed by a variational autoencoder to detect anomalous behavior in the composition phase diagram. This study provides a framework for creating generative models from a combination of multiple experimental data and provides direct insight into the driving forces for cation segregation in manganites.
publisher AMER CHEMICAL SOC
issn 1936-0851
year published 2019
volume 13
issue 1
beginning page 718
ending page 727
digital object identifier (doi) 10.1021/acsnano.8b07980
web of science category Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary
subject category Chemistry; Science & Technology - Other Topics; Materials Science
unique article identifier WOS:000456749900073
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journal impact factor 13.709
5 year journal impact factor 14.820
category normalized journal impact factor percentile 95.274
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