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

Materials & design, 2020-01, Vol.185 (C), p.108247, Article 108247 [Peer Reviewed Journal]

2019 The Authors ;ISSN: 0264-1275 ;EISSN: 1873-4197 ;DOI: 10.1016/j.matdes.2019.108247

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  • Title:
    Robust data-driven approach for predicting the configurational energy of high entropy alloys
  • Author: Zhang, Jiaxin ; Liu, Xianglin ; Bi, Sirui ; Yin, Junqi ; Zhang, Guannan ; Eisenbach, Markus
  • Subjects: Bayesian information criterion ; Bayesian regression ; First-principles calculations ; High entropy alloys ; Machine learning ; MATERIALS SCIENCE ; Uncertainty quantification
  • Is Part Of: Materials & design, 2020-01, Vol.185 (C), p.108247, Article 108247
  • Description: 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. [Display omitted] •A data-driven framework is proposed for predicting the configurational energy of high entropy alloys.•Accuracy and robustness of the model are improved via physical feature selection with Bayesian information criterion.•Uncertainty of the Bayesian regression model is quantified, with robust performance demonstrated.
  • Publisher: United Kingdom: Elsevier Ltd
  • Language: English
  • Identifier: ISSN: 0264-1275
    EISSN: 1873-4197
    DOI: 10.1016/j.matdes.2019.108247
  • Source: DOAJ Directory of Open Access Journals
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