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- Title
Quantum Graph Neural Network Models for Materials Search.
- Authors
Ryu, Ju-Young; Elala, Eyuel; Rhee, June-Koo Kevin
- Abstract
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.
- Subjects
ARTIFICIAL neural networks; QUANTUM graph theory; FRONTIER orbitals; BAND gaps
- Publication
Materials (1996-1944), 2023, Vol 16, Issue 12, p4300
- ISSN
1996-1944
- Publication type
Article
- DOI
10.3390/ma16124300