Featureless adaptive optimization accelerates functional electronic materials design

Electronic materials exhibiting multiple phase transitions between metastable states with distinct physical properties are challenging to decoding using conventional machine learning methods owing to data scarcity and absence of physically meaningful materials descriptors. We demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse.

Reference: Wang et. at., arXiv:2004.07365