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Title: Robust data-driven approach for predicting the configurational energy of high entropy alloys

Abstract

High entropy alloys (HEAs) are promising next-generation materials due to their various excellent properties. To understand these properties, it's necessary to characterize the chemical ordering and identify order-disorder transitions through efficient simulation and modeling of thermodynamics. In this study, a robust data-driven framework based on Bayesian approaches is proposed for the accurate and efficient prediction of configurational energy of high entropy alloys. The recently proposed effective pair interaction (EPI) model with ensemble sampling is used to map the configuration and its corresponding energy. Given limited data calculated by first-principles calculations, Bayesian regularized regression not only offers an accurate and stable prediction but also effectively quantifies the uncertainties associated with EPI parameters. Compared with the arbitrary truncation of model complexity, we further conduct a physical feature selection to identify the truncation of coordination shells in EPI model using Bayesian information criterion. The results achieve efficient and robust performance in predicting the configurational energy, particularly given small data. The developed methodology is applied to study a series of refractory HEAs, i.e. NbMoTaW, NbMoTaWV and NbMoTaWTi where it is demonstrated how dataset size affects the confidence when data is sparse.

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:
1570041
Alternate Identifier(s):
OSTI ID: 1619044
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Published Article
Journal Name:
Materials & Design
Additional Journal Information:
Journal Name: Materials & Design Journal Volume: 185 Journal Issue: C; Journal ID: ISSN 0264-1275
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English
Subject:
36 MATERIALS SCIENCE; High entropy alloys; Uncertainty quantification; Bayesian regression; Bayesian information criterion; First-principles calculations; Machine learning

Citation Formats

Zhang, Jiaxin, Liu, Xianglin, Bi, Sirui, Yin, Junqi, Zhang, Guannan, and Eisenbach, Markus. Robust data-driven approach for predicting the configurational energy of high entropy alloys. United Kingdom: N. p., 2020. Web. doi:10.1016/j.matdes.2019.108247.
Zhang, Jiaxin, Liu, Xianglin, Bi, Sirui, Yin, Junqi, Zhang, Guannan, & Eisenbach, Markus. Robust data-driven approach for predicting the configurational energy of high entropy alloys. United Kingdom. doi:10.1016/j.matdes.2019.108247.
Zhang, Jiaxin, Liu, Xianglin, Bi, Sirui, Yin, Junqi, Zhang, Guannan, and Eisenbach, Markus. Wed . "Robust data-driven approach for predicting the configurational energy of high entropy alloys". United Kingdom. doi:10.1016/j.matdes.2019.108247.
@article{osti_1570041,
title = {Robust data-driven approach for predicting the configurational energy of high entropy alloys},
author = {Zhang, Jiaxin and Liu, Xianglin and Bi, Sirui and Yin, Junqi and Zhang, Guannan and Eisenbach, Markus},
abstractNote = {High entropy alloys (HEAs) are promising next-generation materials due to their various excellent properties. To understand these properties, it's necessary to characterize the chemical ordering and identify order-disorder transitions through efficient simulation and modeling of thermodynamics. In this study, a robust data-driven framework based on Bayesian approaches is proposed for the accurate and efficient prediction of configurational energy of high entropy alloys. The recently proposed effective pair interaction (EPI) model with ensemble sampling is used to map the configuration and its corresponding energy. Given limited data calculated by first-principles calculations, Bayesian regularized regression not only offers an accurate and stable prediction but also effectively quantifies the uncertainties associated with EPI parameters. Compared with the arbitrary truncation of model complexity, we further conduct a physical feature selection to identify the truncation of coordination shells in EPI model using Bayesian information criterion. The results achieve efficient and robust performance in predicting the configurational energy, particularly given small data. The developed methodology is applied to study a series of refractory HEAs, i.e. NbMoTaW, NbMoTaWV and NbMoTaWTi where it is demonstrated how dataset size affects the confidence when data is sparse.},
doi = {10.1016/j.matdes.2019.108247},
journal = {Materials & Design},
number = C,
volume = 185,
place = {United Kingdom},
year = {2020},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1016/j.matdes.2019.108247

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