CAREER: Empirical Design and Precipitation Hardening Pathways in High-Temperature High-Entropy Shape Memory Alloys

NON-TECHNICAL SUMMARY High-entropy alloys (HEAs) are multi-element, near-equal composition alloys pursued for property combinations not typical of conventional alloys. A subset of these alloys undergoes a solid-state phase transformation and therefore shows “shape memory” response, which is a term used to indicate recovery of a preset shape after deformation. High-entropy shape memory alloys (HE-SMAs) are of particular interest as they have the potential to offer greater strength and durability than current systems while operating at higher temperatures while also exhibiting the shape memory response under specific conditions. This project is combining machine learning, practical design rules, and iterative laboratory tests to identify candidate HE-SMA compositions for high-temperature environments. The resulting alloys are being processed further to tailor microstructure and form specific microstructural particles that enhance shape memory response and stability. This study is also examining how particle formation affects the highly sensitive compositional balance in these alloy systems. The broader impacts of this project include: hands-on training for graduate and undergraduate students in modern microscopy, mechanical testing, and data-driven materials design; new course modules which are bringing machine learning into smart materials education; and outreach to high schools via classroom visits, lab tours, and summer internships that are engaging the next generation of engineers. By advancing fundamental understanding of structure-property relationships and integrating data science into alloy design, this work is promoting the progress of science and supporting the national interest consistent with NSF's mission. TECHNICAL SUMMARY This research project is designing high-temperature, high-entropy shape memory alloys (HE-SMAs) by using NiTi as a base composition and substituting elements on the Ni and Ti sublattices. Because the transformation temperatures and thermomechanical responses are highly sensitive to the ratio of Ni-like to Ti-like elements, the project is developing an alloy design framework that is combining empirically derived transformation rules and using machine learning to identify near-pseudo-equiatomic NiCuTiHfZr compositions that balance Ni-like (Ni, Cu) and Ti-like (Ti, Hf, Zr) elements while exhibiting martensitic transformation at elevated temperatures. A critical, yet insufficiently understood, factor in these systems is precipitation hardening, particularly the formation of Ni-rich intermetallic phases that can disrupt compositional balance and alter transformation temperatures, transformation pathways, and shape recovery performance. This study is examining precipitation through systematic aging and investigation of stress-assisted variant selection and precipitate alignment in HE-SMA compositions, where lattice distortion and local chemical inhomogeneity influence microstructural evolution and functional response. Thermomechanical testing is being used to quantify transformation temperatures, hysteresis, recoverable strain, and cyclic stability, while electron microscopy and diffraction experiments are resolving precipitate chemistry, size, and orientation relationships within the matrix. Technically, this project is delivering quantitative linkages among composition, precipitation state, and functional properties while generating data in a currently data-scarce compositional regime. Moreover, the research project is actively developing guidelines for composition selection and microstructure control via targeted precipitation thereby enabling reliable shape recovery at elevated temperatures. By expanding the materials toolbox for high-temperature actuation and resilience, this work is promoting the progress of science, with potential benefits for aerospace, energy, and industrial systems that must operate reliably at high temperatures. Educationally, the broader impacts of this project include: hands-on training for graduate and undergraduate students in modern microscopy, mechanical testing, and data-driven materials design; new course modules which are bringing machine learning into smart materials education; and outreach to high schools via classroom visits, lab tours, and summer internships that are engaging the next generation of engineers. By advancing fundamental understanding of structure-property relationships and integrating data science into alloy design, this work is promoting the progress of science and supporting the national interest consistent with NSF's mission. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.