Bayesian optimization with known experimental and design constraints for chemistry applications
Optimization strategies driven by machine learning, such as Bayesian optimization, are being explored across experimental sciences as an efficient alternative to traditional design of experiment. When combined with automated laboratory hardware and high-performance computing, these strategies enable...
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| Format: | Article |
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2022
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| Online Access: | https://hdl.handle.net/1721.1/146011 |