A general QSPR protocol for the prediction of atomic/inter-atomic properties: a fragment based graph convolutional neural network (F-GCN)

Gao, P, Zhang, J, Qiu, H and Zhao, Shuaifei 2021, A general QSPR protocol for the prediction of atomic/inter-atomic properties: a fragment based graph convolutional neural network (F-GCN), Physical Chemistry Chemical Physics, vol. 23, no. 23, pp. 13242-13249, doi: 10.1039/d1cp00677k.

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Title A general QSPR protocol for the prediction of atomic/inter-atomic properties: a fragment based graph convolutional neural network (F-GCN)
Author(s) Gao, P
Zhang, J
Qiu, H
Zhao, ShuaifeiORCID iD for Zhao, Shuaifei orcid.org/0000-0002-7727-6676
Journal name Physical Chemistry Chemical Physics
Volume number 23
Issue number 23
Start page 13242
End page 13249
Total pages 8
Publisher Royal Society of Chemistry
Place of publication London, Eng.
Publication date 2021
ISSN 1463-9076
1463-9084
Keyword(s) BOND-DISSOCIATION ENERGIES
Chemistry
Chemistry, Physical
DENSITY-FUNCTIONAL THEORY
MOLECULES
Physical Sciences
Physics
Physics, Atomic, Molecular & Chemical
RANGE
REACTIVITY
Science & Technology
SITE
STEREOSELECTIVE FUNCTIONALIZATION
Summary

This study proposed a fragment-based graph convolutional neural network (F-GCN) that can predict atomic and inter-atomic properties and is suitable for few-shot learning.

Language eng
DOI 10.1039/d1cp00677k
Field of Research 02 Physical Sciences
03 Chemical Sciences
09 Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Grant ID Australian Government (Project id: v15) for providing computational resources
Persistent URL http://hdl.handle.net/10536/DRO/DU:30152291

Document type: Journal Article
Collections: Institute for Frontier Materials
GTP Research
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