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cond-mat.mtrl-sci2024
Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks
Zhenyao Fang, Qimin Yan
In machine-learning-assisted high-throughput defect studies, a defect-aware latent representation of the supercell structure is crucial to the accurate prediction of defect propert…
physics.comp-ph2024★ 3 cited
Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals
Alex Heilman, Claire Schlesinger, Qimin Yan
Modern E(3)-Equivariant networks may be used to predict rotationally equivariant properties, including tensorial quantities. Three such quantities: the dielectric, piezoelectric, a…
cond-mat.mtrl-sci2024★ 1 cited
Towards Accurate Prediction of Configurational Disorder Properties in Materials using Graph Neural Networks
Zhenyao Fang, Qimin Yan
The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient an…