Hierarchical Defect Engineering towards Intermetallic Design

• Applicant: Dr.-Ing. Zhuocheng Xie
• Subject Area: Computer-Aided Design of Materials and Simulation of Materials Behaviour from Atomic to Microscopic Scale ; Mechanical Properties of Metallic Materials and their Microstructural Origins
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 562592407

Intermetallic materials, known for their exceptional thermal stability, high-temperature strength, and corrosion resistance, are indispensable for demanding applications in aerospace, energy, and automotive industries. However, their broader adoption has been limited by intrinsic challenges, including brittleness at ambient temperatures and a lack of comprehensive understanding of their complex deformation mechanisms. This project aims to address these barriers through hierarchical defect engineering, an innovative strategy that introduces and precisely manipulates defects across scales—from atomic to macroscopic—unlocking significant advancements in both mechanical and functional properties. By integrating state-of-the-art computational modeling, experimental validation, and machine learning, the project will construct defect phase diagrams and mechanism-property maps. These advanced tools will provide unparalleled insights into how defects influence material behavior, enabling predictive design of intermetallic microstructures optimized for strength, ductility, and fracture toughness. By strategically controlling point defects, dislocations, and grain boundaries, the project will enhance the mechanical robustness of intermetallic systems. For example, tuning atomic-scale defects can improve deformability by reducing critical resolved shear stress, while precise control over grain boundary chemistry and structure can significantly increase fracture resistance, facilitating brittle-to-ductile transitions. Beyond mechanical performance, the project seeks to elevate functional characteristics such as thermal conductivity and magnetic behavior. By introducing targeted defects, it aims to optimize thermoelectric efficiency for waste heat recovery and magnetic coercivity for high-performance applications like electric motors and wind turbines. These advancements are crucial for sustainable energy technologies and next-generation electronic devices, addressing global challenges in energy efficiency and resource utilization. This project marks a paradigm shift from traditional trial-and-error material development to a data-driven, multi-scale approach that accelerates material discovery. Machine learning models, trained on extensive datasets generated from high-throughput simulations and experimental data, will efficiently navigate the vast material design space, identifying optimal configurations with unprecedented speed and precision. By redefining the innovation process for intermetallic materials, this project paves the way for advancements across a wide range of applications, combining superior performance, adaptability, and sustainability to meet the most demanding engineering challenges.

• DFG Programme: Emmy Noether Independent Junior Research Groups