BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces

We present a novel application of Bayesian optimization to the field of surface science: rapidly and accurately searching for the global minimum on potential energy surfaces. Controlling molecule-surface interactions is key for applications ranging from environmental catalysis to gas sensing. We present pragmatic techniques, including exploration/exploitation scheduling and a custom covariance kernel that encodes the properties of our objective function. Our method, the Bayesian Active Site Calculator (BASC), outperforms differential evolution and constrained minima hopping--two state-of-the-art approaches-- in trial examples of carbon monoxide adsorption on a hematite substrate, both with and without a defect.

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