Collaborative Research: DMREF: Discovery and Design of Self-Lubricating Wear-Resistant Metals for High-Temperature Environments

Nontechnical Description: Modern technologies, including aircraft engines, electric power generation, advanced manufacturing, and national defense, require structural materials that can operate reliably under extreme temperatures and low friction. However, most metals lose their wear resistance above ~700 degree C, limiting component lifetime and system reliability. Developing materials that simultaneously retain mechanical strength and resist wear under these conditions remains a fundamental challenge in materials engineering. This research project establishes a new design strategy by developing self-lubricating, wear-resistant complex alloys that intentionally harness oxidation (rather than simply resist it) to form durable, low-friction surfaces. It accelerates the development of next-generation materials for extreme environments while generating open-access datasets and machine learning tools for the broader materials community. The project also provides interdisciplinary training in materials science, advanced manufacturing, and artificial intelligence. Technical Description: This research project tests the central hypothesis that configurational entropy can be exploited to simultaneously stabilize high-strength complex concentrated alloys and entropy-stabilized spinel oxide surface films that exhibit low shear resistance during high-temperature sliding. Rather than treating oxidation as a degradation mechanism, the research aims to establish design principles for engineering oxidation pathways that promote the formation of lubricious spinel oxides instead of abrasive corundum oxides, while preserving bulk mechanical integrity. The project integrates density functional theory, molecular dynamics, phase-field modeling, additive manufacturing, high-throughput experiments, and high-temperature tribological testing within a physics-guided machine-learning framework to establish quantitative processing-structure-oxidation-property-performance relationships. These integrated experimental and computational data enable inverse design of alloy compositions and processing conditions that optimize bulk strength, oxidation behavior, and tribological performance simultaneously. By tightly coupling materials synthesis, characterization, multiscale modeling, and artificial intelligence, the research embodies the objectives of the Designing Materials to Revolutionize and Engineer our Future (DMREF) program, establishing a predictive, data-driven materials design framework that will substantially accelerate the discovery of next-generation self-lubricating alloys for extreme environments. 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.