  Bayesian optimization has emerged as a powerful strategy to accelerate
scientific discovery by means of autonomous experimentation. However, expensive
measurements are required to accurately estimate materials properties, and can
quickly become a hindrance to exhaustive materials discovery campaigns. Here,
we introduce Gemini: a data-driven model capable of using inexpensive
measurements as proxies for expensive measurements by correcting systematic
biases between property evaluation methods. We recommend using Gemini for
regression tasks with sparse data and in an autonomous workflow setting where
its predictions of expensive to evaluate objectives can be used to construct a
more informative acquisition function, thus reducing the number of expensive
evaluations an optimizer needs to achieve desired target values. In a
regression setting, we showcase the ability of our method to make accurate
predictions of DFT calculated bandgaps of hybrid organic-inorganic perovskite
materials. We further demonstrate the benefits that Gemini provides to
autonomous workflows by augmenting the Bayesian optimizer Phoenics to yeild a
scalable optimization framework leveraging multiple sources of measurement.
Finally, we simulate an autonomous materials discovery platform for optimizing
the activity of electrocatalysts for the oxygen evolution reaction. Realizing
autonomous workflows with Gemini, we show that the number of measurements of a
composition space comprising expensive and rare metals needed to achieve a
target overpotential is significantly reduced when measurements from a proxy
composition system with less expensive metals are available.
