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Title: Statistical learning of governing equations of dynamics from in-situ electron microscopy imaging data

Abstract

Recent developments in (scanning) transmission electron microscopy (S)TEM have enabled in-situ investiga-tions of nanoscale transformations. However, understanding the physical and chemical process defining mattertransformations via the analysis of large-scale in-situ (S)TEM imaging data remains challenging. Here, we exper-imentally investigated a reaction-convection-diffusion model to track spatial-temporal patterns in (S)TEMvideos of Pt nanoparticle formation and graphene contamination. Model parameters are pursued by statisticalmodel selection algorithms that balance descriptive capability and model parsimony to aid interpretability andsuppressoverfitting.Besidesconventional bottom-upanalysis fromindividual entities,the integratedmathemat-icalmodel based on partial differentialequations(PDE) utilizing pixel level information provides complementarysystem status that may serve as a feedback for optimizing experiment setting.

Authors:
; ; ; ; ;
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:
1638558
Alternate Identifier(s):
OSTI ID: 1785697
Resource Type:
Published Article
Journal Name:
Materials & Design
Additional Journal Information:
Journal Name: Materials & Design Journal Volume: 195 Journal Issue: C; Journal ID: ISSN 0264-1275
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English
Subject:
36 MATERIALS SCIENCE; 42 ENGINEERING

Citation Formats

Li, Xin, Dyck, Ondrej, Unocic, Raymond R., Ievlev, Anton V., Jesse, Stephen, and Kalinin, Sergei V. Statistical learning of governing equations of dynamics from in-situ electron microscopy imaging data. United Kingdom: N. p., 2020. Web. https://doi.org/10.1016/j.matdes.2020.108973.
Li, Xin, Dyck, Ondrej, Unocic, Raymond R., Ievlev, Anton V., Jesse, Stephen, & Kalinin, Sergei V. Statistical learning of governing equations of dynamics from in-situ electron microscopy imaging data. United Kingdom. https://doi.org/10.1016/j.matdes.2020.108973
Li, Xin, Dyck, Ondrej, Unocic, Raymond R., Ievlev, Anton V., Jesse, Stephen, and Kalinin, Sergei V. Thu . "Statistical learning of governing equations of dynamics from in-situ electron microscopy imaging data". United Kingdom. https://doi.org/10.1016/j.matdes.2020.108973.
@article{osti_1638558,
title = {Statistical learning of governing equations of dynamics from in-situ electron microscopy imaging data},
author = {Li, Xin and Dyck, Ondrej and Unocic, Raymond R. and Ievlev, Anton V. and Jesse, Stephen and Kalinin, Sergei V.},
abstractNote = {Recent developments in (scanning) transmission electron microscopy (S)TEM have enabled in-situ investiga-tions of nanoscale transformations. However, understanding the physical and chemical process defining mattertransformations via the analysis of large-scale in-situ (S)TEM imaging data remains challenging. Here, we exper-imentally investigated a reaction-convection-diffusion model to track spatial-temporal patterns in (S)TEMvideos of Pt nanoparticle formation and graphene contamination. Model parameters are pursued by statisticalmodel selection algorithms that balance descriptive capability and model parsimony to aid interpretability andsuppressoverfitting.Besidesconventional bottom-upanalysis fromindividual entities,the integratedmathemat-icalmodel based on partial differentialequations(PDE) utilizing pixel level information provides complementarysystem status that may serve as a feedback for optimizing experiment setting.},
doi = {10.1016/j.matdes.2020.108973},
journal = {Materials & Design},
number = C,
volume = 195,
place = {United Kingdom},
year = {2020},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.matdes.2020.108973

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