Machine learning made easy for optimizing chemical reactions
Publication type: Journal Article
Publication date: 2021-02-03
scimago Q1
wos Q1
SJR: 18.288
CiteScore: 78.1
Impact factor: 48.5
ISSN: 00280836, 14764687
PubMed ID:
33536642
Multidisciplinary
Abstract
An accessible machine-learning tool has been developed that can accelerate the optimization of a wide range of synthetic reactions — and reveals how cognitive bias might have undermined optimization by humans. Bayesian optimization for synthetic chemistry reactions.
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Metrics
19
Total citations:
19
Citations from 2024:
6
(31.58%)
Cite this
GOST |
RIS |
BibTex |
MLA
Cite this
RIS
Copy
TY - JOUR
DO - 10.1038/d41586-021-00209-6
UR - https://doi.org/10.1038/d41586-021-00209-6
TI - Machine learning made easy for optimizing chemical reactions
T2 - Nature
AU - Hein, Jason E.
PY - 2021
DA - 2021/02/03
PB - Springer Nature
SP - 40-41
IS - 7844
VL - 590
PMID - 33536642
SN - 0028-0836
SN - 1476-4687
ER -
Cite this
BibTex (up to 50 authors)
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@article{2021_Hein,
author = {Jason E. Hein},
title = {Machine learning made easy for optimizing chemical reactions},
journal = {Nature},
year = {2021},
volume = {590},
publisher = {Springer Nature},
month = {feb},
url = {https://doi.org/10.1038/d41586-021-00209-6},
number = {7844},
pages = {40--41},
doi = {10.1038/d41586-021-00209-6}
}
Cite this
MLA
Copy
Hein, Jason E.. “Machine learning made easy for optimizing chemical reactions.” Nature, vol. 590, no. 7844, Feb. 2021, pp. 40-41. https://doi.org/10.1038/d41586-021-00209-6.