Data-driven discovery and rapid, direct synthesis of MXenes

MXenes are a rapidly expanding family of two-dimensional transition metal carbides and nitrides, yet scalable synthesis routes remain limited and historical reports suggest a broader, largely unexplored chemical space. We used machine learning–assisted database mining to uncover hidden multilayer MXenes. Screening reveals 33 previously synthesized but unrecognized MXene candidates. Guided by these findings, we synthesized seven multilayer MXenes using a rapid, scalable self-propagating high-temperature method that requires no sustained external heating and completes within minutes. We further realized 11 previously unexplored rare earth–based MXenes (M = praseodymium, neodymium, samarium, gadolinium, terbium, holmium, and thulium). Experiments and modeling suggest semiconducting behavior and diverse magnetic states across this family. Together, these results expand the MXene family and demonstrate a data-driven strategy for accelerating materials discovery through sustainable methods.