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Electronic materials exhibiting phase transitions between metastable states(e.g., metal-insulator transition materials with abrupt electrical resistivitytransformations) are challenging to decode. For these materials, conventionalmachine learning methods display limited predictive capability due to datascarcity and the absence of features impeding model training. In this article,we demonstrate a discovery strategy based on multi-objective Bayesianoptimization to directly circumvent these bottlenecks by utilizing latentvariable Gaussian processes combined with high-fidelity electronic structurecalculations for validation in the chalcogenide lacunar spinel family. Wedirectly and simultaneously learn phase stability and band gap tunability fromchemical composition alone to efficiently discover all superior compositions onthe design Pareto front. Previously unidentified electronic transitions alsoemerge from our featureless adaptive optimization engine. Our methodologyreadily generalizes to optimization of multiple properties, enabling co-designof complex multifunctional materials, especially where prior data is sparse.

Featureless adaptive optimization accelerates functional electronic materials design