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arXiv · 1806.07475

Learning from imperfections: constructing phase diagrams from atomic imaging of fluctuations

Lukas Vlcek·Maxim A. Ziatdinov·Alexander Tselev·Arthur P. Baddorf·Sergei V. Kalinin·Rama K. Vasudevan
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Abstract

Materials characterization and property measurements are a cornerstone of material science, providing feedback from synthesis to applications. Traditionally, a single sample is used to derive information on a single point in composition space, and imperfections, impurities and stochastic details of material structure are deemed irrelevant or complicating factors in analysis. Here we demonstrate that atomic-scale studies of a single nominal composition can provide information on a finite area of chemical space. This information can be used to reconstruct the material properties in a finite composition and temperature range. We develop a statistical physics-based framework that incorporates chemical and structural data to infer effective atomic interactions driving segregation in a La5/8Ca3/8MnO3 thin-film. A variational autoencoder is used to determine anomalous behaviors in the composition phase diagram. This study provides a framework for creating generative models from diverse data and provides direct insight into the driving forces for cation segregation in manganites.

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Lukas Vlcek, Maxim A. Ziatdinov, Alexander Tselev, Arthur P. Baddorf, Sergei V. Kalinin, Rama K. Vasudevan. 2018-06-19. Learning from imperfections: constructing phase diagrams from atomic imaging of fluctuations. https://arxiv.org/abs/1806.07475

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