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 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.
- Authors:
-
[1];
[2];
[1];
[3];
[2];
[2];
[2]
- 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:
- 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. 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. https://doi.org/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. https://doi.org/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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