Data-driven discovery and rapid, direct synthesis of MXenes.

Machine learning–assisted mining uncovered 33 previously synthesized but unrecognized multilayer MXene candidates, enabling rapid synthesis of seven multilayer MXenes within minutes using a self-propagating high-temperature method.

• Why it matters: Scalable MXene synthesis is limited, and prior reports imply a broader chemical space that remains largely unexplored, slowing discovery of new compositions and properties.
• What they did: The team screened databases with machine learning to identify hidden multilayer candidates, then synthesized seven multilayer MXenes and 11 rare earth–based MXenes using a rapid self-propagating high-temperature approach requiring no sustained external heating.
• The result: Experiments and modeling indicate semiconducting behavior and diverse magnetic states across the expanded MXene family, supporting a data-driven, sustainable route to accelerate materials discovery.

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