A mixed-variable Bayesian optimization framework based on latent variable Gaussian processes is developed and demonstrated on optimizing composition and morphology for insulating polymer nanocomposites, with an extension to multi-objective Pareto optimization.
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Data-Centric Mixed-Variable Bayesian Optimization For Materials Design
A mixed-variable Bayesian optimization framework based on latent variable Gaussian processes is developed and demonstrated on optimizing composition and morphology for insulating polymer nanocomposites, with an extension to multi-objective Pareto optimization.