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

Jiaxin Zhang; Xianglin Liu; Sirui Bi; Junqi Yin; Guannan Zhang; Markus Eisenbach

doi:10.1016/j.matdes.2019.108247

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

Jiaxin Zhang, Xianglin Liu, Sirui Bi, Junqi Yin, Guannan Zhang, Markus Eisenbach

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53 Scopus citations

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.

Original language	English
Article number	108247
Journal	Materials and Design
Volume	185
DOIs	https://doi.org/10.1016/j.matdes.2019.108247
State	Published - Jan 5 2020

Funding

This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).This work of J. Z. was supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory. X. L. and M. E. were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division. This research used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This work of J. Z. was supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory . X. L. and M. E. were supported by the U.S. Department of Energy , Office of Science , Basic Energy Sciences , Materials Science and Engineering Division . This research used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 .

Funders	Funder number
DOE Public Access Plan
Oak
US Department of Energy
UT-Battelle	DE-AC05-00OR22725
U.S. Department of Energy
Office of Science
Basic Energy Sciences
Oak Ridge National Laboratory
Laboratory Directed Research and Development
Division of Materials Sciences and Engineering

Keywords

Bayesian information criterion
Bayesian regression
First-principles calculations
High entropy alloys
Machine learning
Uncertainty quantification

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10.1016/j.matdes.2019.108247

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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.",

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AB - 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.

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

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