Active-learning guided refinement shrinks the design space by 45-50% while retaining over 99% of the Pareto-relevant hypervolume, and warm-started multi-objective Bayesian optimization finds high-value candidates faster.
D., Allaire, D., & Arr´ oyave, R
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Active Learning Guided Design Space Refinement for Scalable Multi-Objective Bayesian Optimization in Materials Discovery
Active-learning guided refinement shrinks the design space by 45-50% while retaining over 99% of the Pareto-relevant hypervolume, and warm-started multi-objective Bayesian optimization finds high-value candidates faster.