Comparative Analysis of Phase Prediction in High-Entropy TiZrNb Alloys Using Machine Learning Approaches

Yousefi, Masoud; Razaghian Arani, Ahmad

doi:10.30501/jamt.2026.569313.1353

Comparative Analysis of Phase Prediction in High-Entropy TiZrNb Alloys Using Machine Learning Approaches

Document Type : Original Reaearch Article

Authors

Masoud Yousefi ¹

Ahmad Razaghian Arani ²

¹ Postdoctoral Researcher in Materials Engineering, Department of Materials Science and Engineering, Faculty of Technical and Engineering, Imam Khomeini International University (IKIU), Qazvin, Iran.

² Professor, Department of Materials Science and Engineering, Faculty of Technical and Engineering, Imam Khomeini International University (IKIU), Qazvin, Iran.

10.30501/jamt.2026.569313.1353

Abstract

A primary challenge in the development of high-entropy alloys (HEAs), particularly biocompatible variants, is the substantial cost associated with experimental trial-and-error procedures. In this study, machine learning (ML) models were employed to predict phase formation—specifically, solid solution (SS), intermetallic (IM), and composite (SS+IM) phases—to minimize experimental overhead and expedite the alloy design process. A reference database comprising approximately 400 HEAs was utilized, with key thermodynamic parameters—namely ΔHmix, ΔSmix, δ, VEC, and Δχ—serving as input features. Two primary learning frameworks were evaluated: the Random Forest (RF) algorithm and ensemble learning techniques, specifically Bagging and Boosting. The results indicate that the three-phase classification accuracy reached over 75% on average using the Random Forest and Bagging methods. This study demonstrates that these ensemble algorithms constitute effective, cost-efficient computational tools for phase prediction in HEAs, significantly accelerating the design of novel alloys tailored for biomedical applications and orthopedic implants.

Keywords

Machine Learning

TiZrNb

High-Entropy Alloy

Phase prediction

Subjects

Other (Simulation, Modelling, Methodology, ...)

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