Performance-oriented multistage design for multi-principal element alloys with low cost yet high efficiency.

Area of Science:

• Materials Science
• Computational Materials Science
• Alloy Design

Background:

• Multi-principal element alloys (MPEAs) show great promise for structural, functional, and smart material applications.
• Designing MPEAs with specific properties is challenging due to the strong dependence on composition and microstructure.
• Existing methods for material development are often time-consuming and lack efficiency in exploring the vast compositional space.

Purpose of the Study:

• To develop an efficient and accurate method for performance-oriented design of MPEAs.
• To address the challenges in identifying target properties and optimal compositions for MPEAs.
• To reduce the time and cost associated with developing novel MPEAs.

Main Methods:

• Proposed a multistage-design approach integrating machine learning, physical laws, and a mathematical model.
• Employed a forward-and-inverse problem-solving strategy for property identification and composition optimization.
• Utilized multistage-design coupling constraints to overcome limitations of one-step design strategies.

Main Results:

• Developed a new multi-phase MPEA with superior strength-ductility synergy.
• Achieved optimal phase fraction and composition through efficient searching.
• Demonstrated significant reductions in development period and cost for MPEA development.

Conclusions:

• The proposed multistage-design approach enables precise tailoring of MPEA composition and microstructure for desired properties.
• This method offers higher efficiency and accuracy compared to existing material development approaches.
• The approach is readily extendable to the development of other multi-principal element materials.