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Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost

Abstract:

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of the...

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Publication status:
Published
Peer review status:
Peer reviewed

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Role:
Author
ORCID:
0000-0002-7928-3722
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Role:
Author
ORCID:
0000-0003-1817-0190
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Role:
Author
ORCID:
0000-0002-4784-7925
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Role:
Author
ORCID:
0000-0001-9846-7140
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Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Sub department:
Organic Chemistry
Oxford college:
St Hilda's College
Role:
Author
ORCID:
0000-0002-0104-4166
Publisher:
Springer Nature
Journal:
Nature Communications More from this journal
Volume:
11
Issue:
1
Article number:
2328
Place of publication:
England
Publication date:
2020-05-11
Acceptance date:
2020-04-15
DOI:
EISSN:
2041-1723
Pmid:
32393773
Language:
English
Keywords:
Pubs id:
1104836
Local pid:
pubs:1104836
Deposit date:
2020-07-30

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