A general QSPR protocol for the prediction of atomic/inter-atomic properties: a fragment based graph convolutional neural network (F-GCN)
journal contribution
posted on 2021-01-01, 00:00 authored by P Gao, J Zhang, H Qiu, Shuaifei ZhaoShuaifei ZhaoThis 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.
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Journal
Physical Chemistry Chemical PhysicsVolume
23Pagination
13242-13249Location
London, Eng.Publisher DOI
ISSN
1463-9076eISSN
1463-9084Language
EnglishPublication classification
C1 Refereed article in a scholarly journalIssue
23Publisher
Royal Society of ChemistryUsage metrics
Keywords
BOND-DISSOCIATION ENERGIESChemistryChemistry, PhysicalDENSITY-FUNCTIONAL THEORYMOLECULESPhysical SciencesPhysicsPhysics, Atomic, Molecular & ChemicalRANGEREACTIVITYScience & TechnologySITESTEREOSELECTIVE FUNCTIONALIZATIONAustralian Government (Project id: v15) for providing computational resources5102 Atomic, molecular and optical physics
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