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- W2908088081 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." @default.
- W2908088081 created "2019-01-11" @default.
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- W2908088081 date "2019-01-04" @default.
- W2908088081 modified "2023-09-27" @default.
- W2908088081 title "Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations" @default.
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- W2908088081 doi "https://doi.org/10.1021/acsnano.8b07980" @default.
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