Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
Phoenics: A Bayesian Optimizer for Chemistry
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A survey of generative crystal modeling, multimodal learning, and closed-loop inverse design pipelines for crystalline solids, including failure modes and evaluation practices.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
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Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
A survey of generative crystal modeling, multimodal learning, and closed-loop inverse design pipelines for crystalline solids, including failure modes and evaluation practices.