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Title: Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations

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 La 5/8Ca 3/8MnO 3 thin film. Here, 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.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [3]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. CICECO - Aveiro Institute of Materials, Aveiro (Portugal)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1505330
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
ACS Nano
Additional Journal Information:
Journal Volume: 13; Journal Issue: 1; Journal ID: ISSN 1936-0851
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; generative model; manganite; scanning tunneling microscopy; segregation; statistical inference; thin film

Citation Formats

Vlcek, Lukas, Ziatdinov, Maxim, Maksov, Artem, Tselev, Alexander, Baddorf, Arthur P., Kalinin, Sergei V., and Vasudevan, Rama K. Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations. United States: N. p., 2018. Web. doi:10.1021/acsnano.8b07980.
Vlcek, Lukas, Ziatdinov, Maxim, Maksov, Artem, Tselev, Alexander, Baddorf, Arthur P., Kalinin, Sergei V., & Vasudevan, Rama K. Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations. United States. doi:10.1021/acsnano.8b07980.
Vlcek, Lukas, Ziatdinov, Maxim, Maksov, Artem, Tselev, Alexander, Baddorf, Arthur P., Kalinin, Sergei V., and Vasudevan, Rama K. Tue . "Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations". United States. doi:10.1021/acsnano.8b07980. https://www.osti.gov/servlets/purl/1505330.
@article{osti_1505330,
title = {Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations},
author = {Vlcek, Lukas and Ziatdinov, Maxim and Maksov, Artem and Tselev, Alexander and Baddorf, Arthur P. and Kalinin, Sergei V. and Vasudevan, Rama K.},
abstractNote = {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. Here, 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.},
doi = {10.1021/acsnano.8b07980},
journal = {ACS Nano},
number = 1,
volume = 13,
place = {United States},
year = {2018},
month = {12}
}

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