Q-DFTNet: A chemistry-informed neural network framework for predicting molecular dipole moments via DFT-Driven QM9 data

This study presents Q-DFTNet, a chemistry-informed neural network (ChINN) framework designed to benchmark graph neural networks (GNNs) for dipole moment prediction using the QM9 dataset. Seven GNN architectures, GCN, GIN, GraphConv, GATConv, GATNet, SAGEConv, and GIN+EdgeConv, were trained for 100 e...

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Bibliographic Details

Main Authors:	Wayo, Dennis Delali Kwesi, Mohd Zulkifli, Mohamad Noor, Ganji, Masoud Darvish, Saporetti, Camila M., Goliatt, Leonardo
Format:	Article
Language:	en
Published:	John Wiley and Sons Inc. 2025
Subjects:	TP Chemical technology
Online Access:	https://umpir.ump.edu.my/id/eprint/47149/1/Q-DFTNet_A%20chemistry-informed%20neural%20network%20framework.pdf https://doi.org/10.1002/jcc.70206 https://umpir.ump.edu.my/id/eprint/47149/
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