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Bayesian optimization has emerged as a powerful strategy for solving complex optimization problems when direct gradient information is unavailable or when objective evaluations are expensive. This talk provides a concise introduction to the theoretical foundations of Bayesian optimization, illustrates its superiority in various problem settings, and explores how it can be leveraged to optimize pseudopotentials for plane-wave DFT.
Laboratory for Materials Simulations (LMS)