|
authors |
Vlcek, L; Ziatdinov, M; Maksov, A; Tselev, A; Baddorf, AP; Kalinin, SV; Vasudevan, RK |
|
nationality |
International |
|
journal |
ACS NANO |
|
author keywords |
scanning tunneling microscopy; statistical inference; generative model; segregation; manganite; thin film |
|
keywords |
STATISTICAL DISTANCE; MANGANITES; INFRASTRUCTURE; IDENTIFICATION; SEGREGATION; EXTRACTION; SURFACES; ELECTRON; PHYSICS; SEARCH |
|
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. |
|
publisher |
AMER CHEMICAL SOC |
|
issn |
1936-0851 |
|
isbn |
1936-086X |
|
year published |
2019 |
|
volume |
13 |
|
issue |
1 |
|
beginning page |
718 |
|
ending page |
727 |
|
digital object identifier (doi) |
10.1021/acsnano.8b07980 |
|
web of science category |
Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary |
|
subject category |
Chemistry; Science & Technology - Other Topics; Materials Science |
|
unique article identifier |
WOS:000456749900073
|
