High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength.

Authors

Chao Xu, Dezhi Chen, Xiaofu Zhang, Qi Wang, Jingyue Yu, Shu Wang, Jingjie Guo, Hengzhi Fu, Ruirun Chen, Turab Lookman

Published in

Nature communications. Jul 10, 2026. Epub Jul 10, 2026.

Abstract

High temperature Nb-Si based alloys face a critical challenge: achieving adequate room-temperature fracture toughness ( > 18 MPa·m^1/2) for processing while maintaining high-temperature strength, properties that typically compete with each other. Here, we overcome this inherent trade-off through machine learning-guided alloy design, employing a three-step feature screening strategy to identify 6 key descriptors from 200 initial features. SHAP analysis reveals how melting enthalpy and atomic radius mismatch control property outcomes, enabling targeted multi-objective optimization via NSGA-II algorithm. The optimized Nb-12.26Si-21.35Ti-1.98Al-1.96Cr-0.51Hf-4.34Zr-4.35 V alloy achieves an as-cast fracture toughness of 18.92 MPa·m^1/2 while maintaining 322 MPa strength at 1250 °C, surpassing all reported as-cast Nb-Si alloys. Microstructural analysis shows that the superior properties originate from the dispersed distribution of nanoscale γ'-Nb₅Si₃ phase and crack deflection at phase boundaries with 67.6% lattice mismatch. Our results demonstrate that combining machine learning techniques with mechanistic understanding can accelerate the discovery of high temperature materials.

PMID:
42425984
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.