Symmetrical Graph Neural Network for Quantum Chemistry, with Dual R/K Space

Most of current neural network models in quantum chemistry (QC) exclude the molecular symmetry, separate the well-correlated real space (R space), and momenta space (K space) into two individuals, which lack the essential physics in molecular chemistry. In this work, by endorsing the molecular symme...

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Bibliographic Details
Published in:arXiv.org 2019-12
Main Authors: Ye, Shuqian, Liang, Jiechun, Liu, Rulin, Zhu, Xi
Format: Article
Language:English
Subjects:
Chemical reactions
Chemistry
Graph neural networks
Group theory
Machine learning
Neural networks
Organic chemistry
Performance prediction
Quantum chemistry
Symmetry
Online Access:Get full text
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Summary:Most of current neural network models in quantum chemistry (QC) exclude the molecular symmetry, separate the well-correlated real space (R space), and momenta space (K space) into two individuals, which lack the essential physics in molecular chemistry. In this work, by endorsing the molecular symmetry and elementals of group theory, we propose a comprehensive method to apply symmetry in the graph neural network (SY-GNN), which extends the property-predicting coverage to all the orbital symmetry for both ground and excited states. SY-GNN shows excellent performance in predicting both the absolute and relative of R and K spaces quantities. Besides the numerical properties, SY-GNN also can predict the orbitals distributions in real space, providing the active regions of chemical reactions. We believe the symmetry endorsed deep learning scheme covers the significant physics inside and is essential for the application of neural networks in QC and many other research fields in the future.
ISSN:2331-8422
DOI:10.48550/arxiv.1912.07256