Machine learning-assisted design of lightweight refractory high-entropy alloys: A comprehensive review

Lightweight refractory high-entropy alloys (LRHEAs) represent an emerging class of structural materials that integrate low density with exceptional strength and outstanding high-temperature stability, positioning them as promising candidates for aerospace and advanced industrial applications. Nevertheless, the design of LRHEAs is challenged by their vast compositional space, complex multi-objective performance trade-offs, and the inefficiency of conventional trial-and-error experimental approaches. In recent years, machine learning (ML) has emerged as a transformative tool in this domain, offering the capacity to analyze high-dimensional datasets and uncover hidden correlations between composition, processing,…

• NN
  National Natural Science Foundation of China

  Award: 52574400
• NU
  National University's Basic Research Foundation of China
• DO
  David O. McKay School of Education, Brigham Young University
• NO
  National Outstanding Youth Science Fund Project of National Natural Science Foundation of China

  Award: 51825401
• FR
  Fundamental Research Funds for the Central Universities