  Decoherence in superconducting qubits has long been attributed to two level
systems arising from the surfaces and interfaces present in real devices. A
recent significant step in reducing decoherence was the replacement of
superconducting niobium by superconducting tantalum, resulting in a tripling of
transmon qubit lifetimes (T1). One of these surface variables, the identity,
thickness, and quality of the native surface oxide, is thought to play a major
role as tantalum only has one oxide whereas niobium has several. Here we report
the development of a thermodynamic metric to rank materials based on their
potential to form a well-defined, thin, surface oxide. We first compute this
metric for known binary and ternary metal alloys using data available from
Materials Project, and experimentally validate the strengths and limits of this
metric through preparation and controlled oxidation of 8 known metal alloys.
Then we train a convolutional neural network to predict the value of this
metric from atomic composition and atomic properties. This allows us to compute
the metric for materials that are not present in materials project, including a
large selection of known superconductors, and, when combined with Tc, allow us
to identify new candidate superconductors for quantum information science
(QISE) applications. We test the oxidation resistance of a pair of these
predictions experimentally. Our results are expected to lay the foundation for
tailored and rapid selection of improved superconductors for QISE.
