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.