High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength
4 affiliations
- 1National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin, PR China
- 2National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin, PR China. [email protected]
- 3National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin, PR China. [email protected]
- 4AiMaterials Research LLC, Santa Fe, NM, USA. [email protected]
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′-Nb 5 Si 3 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.
Related
- The Role of Carbon Fibers in FFF 3D Printed Composites: A Comprehensive Review on the Effect on Mechanical Properties, Fracture Toughness, and Tribological Performance
- Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness
- Increasing both strength and toughness in ceramic-matrix composites via bioinspired porous interphases
- Reactive Compatibilization of Polyglycolic Acid/Polyurethane Blends for an Improved Strength–Toughness Balance
- Experimental and Numerical Investigation of the Interfacial Bonding in Multi-Material Fused-Filament Fabrication Using Mode-I Fracture Toughness
BibTeX
@article{xu2026high,
title = {{High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength}},
author = {Chao Xu and Dezhi Chen and Xiaofu Zhang and Qi Ming Wang and Jingyue Yu and Shu Wang and Jingjie Guo and Hengzhi Fu and Ruirun Chen and Turab Lookman},
journal = {Nature Communications},
year = {2026},
month = jul,
doi = {10.1038/s41467-026-75353-6},
url = {https://doi.org/10.1038/s41467-026-75353-6},
note = {CC BY},
}
RIS
TY - JOUR
TI - High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength
AU - Chao Xu
AU - Dezhi Chen
AU - Xiaofu Zhang
AU - Qi Ming Wang
AU - Jingyue Yu
AU - Shu Wang
AU - Jingjie Guo
AU - Hengzhi Fu
AU - Ruirun Chen
AU - Turab Lookman
PY - 2026
DA - 2026/07/10
JO - Nature Communications
DO - 10.1038/s41467-026-75353-6
UR - https://doi.org/10.1038/s41467-026-75353-6
AB - 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′-Nb 5 Si 3 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.
SN - 2041-1723
ER -