Quantum Graph Neural Network Models for Materials Search

Quantum Graph Neural Network Models for Materials Search

Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular orbital...

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Bibliographic Details
Published in:Materials Vol. 16; no. 12; p. 4300
Main Authors: Ryu, Ju-Young, Elala, Eyuel, Rhee, June-Koo Kevin
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 10-06-2023
MDPI
Subjects:
Algorithms
Circuits
Comparative analysis
Crystals
Energy gap
Graph neural networks
Graphs
Integrated circuits
Machine learning
materials search
Molecular orbitals
Neural networks
Organic chemistry
Physical properties
Quantum computing
quantum graph neural networks
quantum machine learning
Semiconductor chips
materials search
quantum machine learning
quantum graph neural networks
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Summary:Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular orbitals of small organic molecules. The models utilize the equivariantly diagonalizable unitary quantum graph circuit (EDU-QGC) framework to allow discrete link features and minimize quantum circuit embedding. The results show QGNNs can achieve lower test loss compared to classical models if a similar number of trainable variables are used, and converge faster in training. This paper also provides a review of classical graph neural network models for materials research and various QGNNs.
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ISSN:1996-1944
1996-1944
DOI:10.3390/ma16124300