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Bayesian optimization with known experimental and design constraints for chemistry applications

Hickman, Riley J., Aldeghi, Matteo, Häse, Florian, Aspuru-Guzik, Alán·2022·Digital Discovery
DOI 10.1039/d2dd00028h
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Abstract

A Bayesian optimization algorithm that satisfies known constraints has been developed. The usefulness of considering experimental and design constraints are shown in two simulated chemistry applications.

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Cited by

Sorted by citations

Authors
Hickman, R. J., Aldeghi, M., Häse, F., & Aspuru-Guzik, A.
Affiliations
Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada
ORCID
Aldeghi, Matteo
Venue
Digital Discovery
Publisher
Royal Society of Chemistry (RSC)
Published
2022
Volume
1
Issue
5
Pages
732-744
ISSN
2635-098X
DOI
10.1039/d2dd00028h
Cited by
92
References
4
Language
en
Licence
creativecommons.org/licenses/by/3.0/ (vor, 256 d)
Funders
  • Office of Naval Research (N00014-18-S-B-001)
  • Natural Sciences and Engineering Research Council of Canada (PGSD3-534584-2019)
  • Canadian Institute for Advanced Research

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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