Extrapolative Bayesian optimization with Gaussian process and neural network ensemble surrogate models

Extrapolative Bayesian optimization with Gaussian process and neural network ensemble surrogate models

Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of experimental parameters in automated active learning driven high throughput experiments in materials science and chemistry. Previous studies suggest that optimization performance of the typical surrogate m...

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Bibliographic Details
Main Authors: Lim, Yee-Fun, Ng, Chee Koon, Vaitesswar, U. S., Hippalgaonkar, Kedar
Other Authors: School of Materials Science and Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Materials
Automated Experiments
Bayesian Optimization
Online Access:https://hdl.handle.net/10356/159296

Internet

https://hdl.handle.net/10356/159296

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