Machine Learning Based Prediction of Phases in High-Entropy Alloys

15 Pages Posted: 2 Mar 2020

See all articles by Ronald Machaka

Ronald Machaka

Council for Scientific and Industrial Research; School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg

Date Written: January 22, 2020

Abstract

Intro: The vastness of high-entropy alloys (HEAs) compositional space and breadth of microstructures makes it possible to tailor properties required by an application. Predicting the phase formation is therefore important in the design of future HEAs. Machine learning based studies on high-entropy alloys are still relatively scarce and non-standardized.

Objectives: this paper reports on the implementation of ML-based classifiers techniques compares the performance of Decision Tree (DT) and Random Forest (RF) for the prediction of phase formation in HEAs.

Results: A new dataset based on 1460 microstructural observations collected from 418 peer-reviewed studies was curated. It contains 36 metallurgy-specific predictor features and a dependent variable, which referred to the phase formation. Based on recursive feature-elimination algorithm an expansive collection of predictive feature for future studies is proposed in addition to widely employed features such difference in the electro-negativities (Δχ) and atomic size (δ), valence electron concentration (VEC), mixing enthalpy (ΔHm), and configuration entropy (ΔSm). The RF model yields higher discriminative performance compared to the DT classifier as well as other tree-based benchmark models. The RF model should be the first choice for investigators when building classification. All models shows lower classification accuracy when intermetallic compounds.

Summary: These results suggest classic machine learning algorithms may be sufficient to develop high quality predictive models of phase formation in HEAs.

Keywords: Machine Learning, Materials Informatics, Multi-Class Classification; High-Entropy Alloys; High Entropy Alloys; Phase Prediction; Data‐driven science

Suggested Citation

Machaka, Ronald, Machine Learning Based Prediction of Phases in High-Entropy Alloys (January 22, 2020). Available at SSRN: https://ssrn.com/abstract=3530328 or http://dx.doi.org/10.2139/ssrn.3530328

Ronald Machaka (Contact Author)

Council for Scientific and Industrial Research ( email )

Meiring Naudé Road
Brummeria
Pretoria, Gauteng 0184
South Africa

School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg ( email )

Doornfontein Campus
Doornfontein
Johannesburg, Gauteng 2050
South Africa

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