Thermodynamic and Electronic Descriptor-Driven Machine Learning for Phase Prediction in High-Entropy Alloys: Experimental Validation
Year: 2025
Type: article
Abstract: High-entropy alloys (HEAs) exhibit complex phase formation behavior, challenging conventional predictive methods. This study presents a machine learning (ML) framework for phase prediction in HEAs, using a curated dataset of 648 experimentally characterized compositions and features derived from thermodynamic and electronic descriptors. Three classifiers—random forest, gradient boosting, and CatBoost—were trained and validated through cross-validation and testing. Gradient boosting achieved the highest accuracy, and valence electron concentration (VEC), atomic size mismatch (δ), and enthalpy of mixing (ΔHmix) were identified as the most influential features. The model predictions were experimentally verified using a non-equiatomic Al₃₀Cu₁₇.₅Fe₁₇.₅Cr₁₇.₅Mn₁₇.₅ alloy and the equiatomic Cantor alloy (CoCrFeMnNi), both of which showed strong agreement with predicted phase structures. The results demonstrate that combining physically informed feature engineering with ML enables accurate and generalizable phase prediction, supporting accelerated HEA design.
Source: Hanguk bunmal jaeryo hakoeji
Authors: Nguyen Lam Khoa, Nguyen Duy Khanh, Hoang Thi Ngoc Quyen, Nguyen Thi Hoang, Oanh, Le Hong Thang, Nguyen Hoa Khiem, Nguyen Hoang Viet
Language: English
Topic: High Entropy Alloys Studies
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Funder: Bộ Giáo dục và Ðào tạo
OpenAlex ID: W4411778564