dc.contributor.author | Liang, Qiaohao | |
dc.contributor.author | Gongora, Aldair E | |
dc.contributor.author | Ren, Zekun | |
dc.contributor.author | Tiihonen, Armi | |
dc.contributor.author | Liu, Zhe | |
dc.contributor.author | Sun, Shijing | |
dc.contributor.author | Deneault, James R | |
dc.contributor.author | Bash, Daniil | |
dc.contributor.author | Mekki-Berrada, Flore | |
dc.contributor.author | Khan, Saif A | |
dc.contributor.author | Hippalgaonkar, Kedar | |
dc.contributor.author | Maruyama, Benji | |
dc.contributor.author | Brown, Keith A | |
dc.contributor.author | Fisher III, John | |
dc.contributor.author | Buonassisi, Tonio | |
dc.date.accessioned | 2021-12-14T19:19:24Z | |
dc.date.available | 2021-12-14T19:19:24Z | |
dc.date.issued | 2021-12 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/138478 | |
dc.description.abstract | <jats:title>Abstract</jats:title><jats:p>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 collection of surrogate model and acquisition function pairs across five diverse experimental materials systems. By defining acceleration and enhancement metrics for materials optimization objectives, we find that surrogate models such as Gaussian Process (GP) with anisotropic kernels and Random Forest (RF) have comparable performance in BO, and both outperform the commonly used GP with isotropic kernels. GP with anisotropic kernels has demonstrated the most robustness, yet RF is a close alternative and warrants more consideration because it is free from distribution assumptions, has smaller time complexity, and requires less effort in initial hyperparameter selection. We also raise awareness about the benefits of using GP with anisotropic kernels in future materials optimization campaigns.</jats:p> | en_US |
dc.language.iso | en | |
dc.publisher | Springer Science and Business Media LLC | en_US |
dc.relation.isversionof | 10.1038/s41524-021-00656-9 | en_US |
dc.rights | Creative Commons Attribution 4.0 International license | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Nature | en_US |
dc.title | Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Liang, Qiaohao, Gongora, Aldair E, Ren, Zekun, Tiihonen, Armi, Liu, Zhe et al. 2021. "Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains." npj Computational Materials, 7 (1). | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Materials Science and Engineering | |
dc.contributor.department | Massachusetts Institute of Technology. Research Laboratory of Electronics | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mechanical Engineering | |
dc.contributor.department | Singapore-MIT Alliance in Research and Technology (SMART) | |
dc.relation.journal | npj Computational Materials | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2021-12-14T19:13:49Z | |
dspace.orderedauthors | Liang, Q; Gongora, AE; Ren, Z; Tiihonen, A; Liu, Z; Sun, S; Deneault, JR; Bash, D; Mekki-Berrada, F; Khan, SA; Hippalgaonkar, K; Maruyama, B; Brown, KA; Fisher III, J; Buonassisi, T | en_US |
dspace.date.submission | 2021-12-14T19:13:51Z | |
mit.journal.volume | 7 | en_US |
mit.journal.issue | 1 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |