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Bayesian optimization has emerged as a powerful strategy to acceleratescientific discovery by means of autonomous experimentation. However, expensivemeasurements are required to accurately estimate materials properties, and canquickly become a hindrance to exhaustive materials discovery campaigns. Here,we introduce Gemini: a data-driven model capable of using inexpensivemeasurements as proxies for expensive measurements by correcting systematicbiases between property evaluation methods. We recommend using Gemini forregression tasks with sparse data and in an autonomous workflow setting whereits predictions of expensive to evaluate objectives can be used to construct amore informative acquisition function, thus reducing the number of expensiveevaluations an optimizer needs to achieve desired target values. In aregression setting, we showcase the ability of our method to make accuratepredictions of DFT calculated bandgaps of hybrid organic-inorganic perovskitematerials. We further demonstrate the benefits that Gemini provides toautonomous workflows by augmenting the Bayesian optimizer Phoenics to yeild ascalable optimization framework leveraging multiple sources of measurement.Finally, we simulate an autonomous materials discovery platform for optimizingthe activity of electrocatalysts for the oxygen evolution reaction. Realizingautonomous workflows with Gemini, we show that the number of measurements of acomposition space comprising expensive and rare metals needed to achieve atarget overpotential is significantly reduced when measurements from a proxycomposition system with less expensive metals are available.

Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation