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

Vlcek, Lukas; Ziatdinov, Maxim; Maksov, Artem; Tselev, Alexander; Baddorf, Arthur P.; Kalinin, Sergei V.; Vasudevan, Rama K.

doi:10.1021/acsnano.8b07980

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_3/8MnO₃ 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.

Authors:

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
CICECO - Aveiro Institute of Materials, Aveiro (Portugal)

Publication Date:: 2018-12-04

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

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.  https://doi.org/10.1021/acsnano.8b07980.

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                    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.  https://doi.org/10.1021/acsnano.8b07980

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                    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.  https://doi.org/10.1021/acsnano.8b07980.  https://www.osti.gov/servlets/purl/1505330.

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@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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Works referencing / citing this record:

Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
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Vasudevan, Rama K.; Choudhary, Kamal; Mehta, Apurva
MRS Communications, Vol. 9, Issue 3
DOI: 10.1557/mrc.2019.95

Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
journal, July 2019

Vasudevan, Rama K.; Choudhary, Kamal; Mehta, Apurva
MRS Communications, Vol. 9, Issue 3
DOI: 10.1557/mrc.2019.95

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Citation Formats

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Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics journal, July 2019

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