4.7 Article

Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study

Journal

MATERIALS & DESIGN
Volume 202, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2021.109532

Keywords

Machine learning; High entropy alloy; CALPHAD; Solid solution; Phase selection rules

Funding

  1. RIE2020 AME Programmatic Grant: AMDM [A1898b0043]

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This study presents new phase selection rules for high entropy alloys (HEAs) by combining CALPHAD calculations and the machine learning (ML) method. The eXtreme Gradient Boosting (XGBoost) method is used to identify 5 important descriptors for delineating single and mixed phases in HEAs. The established rules offer a success rate above 90% in predicting single FCC and BCC phases, outperforming existing rules and providing a powerful tool for mapping single-phase regions in the complex temperature-composition space of HEAs.
We reveal high-fidelity new phase selection rules for high entropy alloys (HEAs) by combining CALPHAD calculations and the machine learning (ML) method. Employing Thermo-Calc and TCHEA3 database, we first generate more than 300,000 equilibrium phase data from 20 quinary families formed by the 8 elements of Al Co, Cr, Cu, Fe, Mn, Ni, and Ti, and choose initially 15 materials/physical descriptors. The eXtreme Gradient Boosting (XGBoost) method is then used to identify 5 most important descriptors that best delineate the single and mixed phases in the complex temperature-composition space of HEAs. The ML model trained by the 5 features is validated by 155 annealing experimental data points from 15 publications and then used to predict 213 new single-phase alloys with BCC and FCC structures of the alloy families of AlCrNiFeMn and AlCrCoNiFeTi. We also highlight the importance of equilibrium temperature and offer in-depth insights into the paradigm of composition-feature-phase of HEAs. On the basis of the 5 important features, we establish new phase selection rules for single FCC and BCC phases with a success rate above 90%, significantly outperforming all existing phase selection rules and providing a powerful tool for mapping single-phase in the complex temperature-composition space of HEAs. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

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