Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains

Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains

Abstract Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains. In this work, we quantify the performance of BO with a collecti...

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Bibliographic Details
Main Authors: Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III, Tonio Buonassisi
Format: Article
Language:English
Published: Nature Portfolio 2021-11-01
Series:npj Computational Materials
Online Access:https://doi.org/10.1038/s41524-021-00656-9

Internet

https://doi.org/10.1038/s41524-021-00656-9

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