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Statistical Physics-based Framework and Bayesian Inference for Model Selection and Uncertainty Quantification

Published online by Cambridge University Press:  05 August 2019

Lukas Vlcek
Affiliation:
Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN, USA. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge TN, USA.
Shize Yang
Affiliation:
Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN, USA. Brookhaven National Laboratory.
Maxim Ziatdinov
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN, USA. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge TN, USA. Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge TN, USA.
Sergei Kalinin
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN, USA. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge TN, USA.
Rama Vasudevan*
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN, USA. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge TN, USA.
*
*Corresponding author: [email protected]

Abstract

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Type
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Vlcek et al. , J. Chem. Theo. Comp. 13 (2017), p. 5179.Google Scholar
[2]Vlcek et al. , ACS Nano 11 (2017), p. 10313.Google Scholar
[3]Vlcek et al. , ACS Nano 13 (2019), p. 718.Google Scholar
[4]This work was supported by the U.S. Department of Energy, Office of Science, Materials Sciences and Engineering Division (LV, SVK, RKV). Research was conducted at the Center for Nanophase Materials Sciences, which also provided support (MZ) and is a US DOE Office of Science User Facility.Google Scholar