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