Featureless adaptive optimization accelerates functional electronic materials design

Yiqun Wang, Akshay Iyer, Wei Chen, James M. Rondinelli*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, 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.

Original languageEnglish (US)
Article number041403
JournalApplied Physics Reviews
Issue number4
StatePublished - Dec 1 2020

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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