Predicting the Materials Properties Using a 3D Graph Neural Network With Invariant Representation

Accurate prediction of physical properties is critical for discovering and designing novel materials. Machine learning technologies have attracted significant attention in the materials science community for their potential for large-scale screening. Graph Convolution Neural Network (GCNN) is one of...

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Veröffentlicht in:	IEEE access 2022, Vol.10, p.62440-62449
Hauptverfasser:	Zhang, Boyu, Zhou, Mushen, Wu, Jianzhong, Gao, Fuchang
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Artificial neural networks Atomic interactions Atomic states Convolution Convolutional neural networks Crystals Graph convolution neural network Graph neural networks Henry’s constant Interatomic forces Machine learning Material properties Materials science Materials science and technology Metal-organic frameworks Neural networks Physical properties Predictive models property prediction Solid modeling Spatial distribution
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