Synergy of machine learning and density functional theory calculations for predicting experimental Lewis base affinity and Lewis polybase binding atoms.
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%0 Journal Article
%1 journals/jcc/HuynhLVNHFP24
%A Huynh, Hieu
%A Le, Khanh
%A Vu, Linh
%A Nguyen, Trang
%A Holcomb, Matthew
%A Forli, Stefano
%A Phan, Hung
%D 2024
%J J. Comput. Chem.
%K dblp
%N 18
%P 1552-1561
%T Synergy of machine learning and density functional theory calculations for predicting experimental Lewis base affinity and Lewis polybase binding atoms.
%U http://dblp.uni-trier.de/db/journals/jcc/jcc45.html#HuynhLVNHFP24
%V 45
@article{journals/jcc/HuynhLVNHFP24,
added-at = {2024-06-18T00:00:00.000+0200},
author = {Huynh, Hieu and Le, Khanh and Vu, Linh and Nguyen, Trang and Holcomb, Matthew and Forli, Stefano and Phan, Hung},
biburl = {https://www.bibsonomy.org/bibtex/25b602edc742e5928e09f5cfeec3374fe/dblp},
ee = {https://doi.org/10.1002/jcc.27329},
interhash = {f3f04b9557fc6b31fdc81c6bf868b411},
intrahash = {5b602edc742e5928e09f5cfeec3374fe},
journal = {J. Comput. Chem.},
keywords = {dblp},
month = {July},
number = 18,
pages = {1552-1561},
timestamp = {2024-06-24T07:08:34.000+0200},
title = {Synergy of machine learning and density functional theory calculations for predicting experimental Lewis base affinity and Lewis polybase binding atoms.},
url = {http://dblp.uni-trier.de/db/journals/jcc/jcc45.html#HuynhLVNHFP24},
volume = 45,
year = 2024
}
