Active learning design of bcc solid solution alloys with gigapascal strength and elemental metal–level ductility
Abstract
Body-centered cubic (bcc) alloys can achieve gigapascal-level yield strengths but typically are limited in tensile ductility (<20%), contrasting sharply with elemental metals (the largest elongation of ~50%). Multi-principal-element alloys offer vast compositional space to reach synergistic strength–ductility combinations. However, combinatorial trial-and-error exploration is prohibitively costly, while machine learning (ML) approaches are hindered by data scarcity. Here, we develop an ML-guided framework integrating active learning with physics-informed Bayesian optimization to rapidly converge on optimal compositions. The resulting Ti 36 V 14 Nb 22 Hf 22 Zr 1 Al 5 alloy achieves a yield strength of 953 MPa and a large tensile ductility of 42%. The high strength arises from the substantial lattice distortion, as well as the ~1-nm-sized local chemical fluctuations (LCFs) inherent to the highly concentrated bcc solid solution. The ubiquitous LCFs also substantially promote dislocation multiplication and strain hardening, enabling a large tensile ductility. Our approach demonstrates ML’s efficacy in accelerating the finding of high-performance alloys.
Article Details
Journal Info
Proceedings of the National Academy of Sciences
National Academy of Sciences
Authors (9)
Zhixing Wang
Center for Alloy Innovation and Design, Center for Advancing Materials Performance from the Nanoscale and Hysitron Applied Research Center in China, State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi’an Jiaotong University
Xiangyue Chen
National Engineering Research Center of Light Alloy Net Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University
Dongqing Zhang
Center for Alloy Innovation and Design, Center for Advancing Materials Performance from the Nanoscale and Hysitron Applied Research Center in China, State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi’an Jiaotong University
Ge Wu
Center for Alloy Innovation and Design, Center for Advancing Materials Performance from the Nanoscale and Hysitron Applied Research Center in China, State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi’an Jiaotong University
Yan Ma
Xiaoqin Zeng
National Engineering Research Center of Light Alloy Net Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University
Ziyuan Rao
National Engineering Research Center of Light Alloy Net Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University
Chang Liu
Evan Ma
Center for Alloy Innovation and Design, State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University