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Materials with higher operating temperatures than today's state of the artcan improve system performance in several applications and enable newtechnologies. Under most scenarios, a protective oxide scale with high meltingtemperatures and thermodynamic stability as well as low ionic diffusivity isrequired. Thus, the design of high-temperature systems would benefit fromknowledge of these properties and related ones for all known oxides. While someproperties of interest are known for many oxides (e.g. elastic constants existfor over 1,000 oxides), melting temperature is known for a relatively smallsubset. The determination of melting temperatures is time consuming and costly,both experimentally and computationally, thus we use data science tools todevelop predictive models from the existing data. The relatively small numberof available melting temperature values precludes the use of standard tools;therefore, we use a multi-step approach based on sequential learning wheresurrogate data from first-principles calculations is leveraged to developmodels using small datasets. We use these models to predict the desiredproperties for nearly 11,000 oxides and quantify uncertainties in the space.

Expanding materials selection via transfer learning for high-temperature oxide selection