The U.S. National Science Foundation (NSF) has awarded $2 million to a joint research project led by Virginia Tech with Arizona State University and Iowa State University to develop self-lubricating, wear-resistant metal alloys for extreme-temperature applications. The project combines additive manufacturing with computational modeling, high-throughput testing, and machine learning. The goal is to identify alloy systems that maintain structural strength at high temperatures while providing self-lubrication at friction interfaces. Conventional lubricants fail at high temperatures, whereas self-lubricating alloys achieve oil-free lubrication through lubricating phases formed within their microstructure. This is significant for high-temperature moving components in aerospace engines, gas turbines, and nuclear power. High-throughput additive manufacturing trials combined with machine learning screening can greatly accelerate the exploration of new alloy composition-process-performance relationships. This approach is directly linked to processes such as powder bed fusion and is expected to yield dedicated powder grades suitable for LPBF/DED. Value for powder buyers: ① If self-lubricating high-temperature alloys are engineered, they will create small-batch demand for custom-composition pre-alloyed powders; manufacturers with rapid powder production and small-batch delivery capabilities should watch for material spillovers from such R&D projects. ② The R&D paradigm of machine learning plus high-throughput trials accelerates alloy iteration, allowing powder companies to prepare in advance for flexible composition powder production. ③ In the short term, this is a cutting-edge R&D signal; we recommend technical tracking rather than immediate stockpiling.