Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs

Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs

The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in mai...

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Bibliographic Details
Main Authors: Low, Andre Kai Yuan, Mekki-Berrada, Flore, Gupta, Abhishek, Ostudin, Aleksandr, Xie, Jiaxun, Vissol-Gaudin, Eleonore, Lim, Yee-Fun, Li, Qianxiao, Ong, Yew Soon, Khan, Saif A., Hippalgaonkar, Kedar
Other Authors: School of Materials Science and Engineering
Format: Article
Language:English
Published: 2024
Subjects:
Engineering
Bayesian optimization
Multi-objective problem
Online Access:https://hdl.handle.net/10356/178838
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Institution: Nanyang Technological University
Language: English

Internet

https://hdl.handle.net/10356/178838

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