Synergistic prediction of phase and hardness in high-entropy alloys via the integration of machine learning and active learning strategies

Hao Liu; Zhihao Shen; W L Wang; Jian Liu; P Chen; Haifeng Yang; Jingbin Hao; Yanhua Bian

doi:10.1039/d6ta01000h

Synergistic prediction of phase and hardness in high-entropy alloys via the integration of machine learning and active learning strategies

Journal of Materials Chemistry A 2026

View at publisher

Authors: Hao Liu, Zhihao Shen, W L Wang, Jian Liu, P Chen, Haifeng Yang, Jingbin Hao, Yanhua Bian

DOI: 10.1039/d6ta01000h

Cited by: 1

References: 44

Abstract

Due to the extensive compositional space and limited sample size, achieving comprehensive and accurate predictions of the microstructure and properties of high-entropy alloys (HEAs) using machine learning models remains a challenging task.

Fields of Study & Subjects

High Entropy Alloys Studies Machine Learning in Materials Science Additive Manufacturing Materials and Processes

Publication details

Journal / source: Journal of Materials Chemistry A
Publisher: Royal Society of Chemistry (RSC)
Volume: 14
Issue: 39
Pages: 26282–26301
Publication date: 1 January 2026
Language: EN

References 44