2 papers
cond-mat.mtrl-sci2025
Unveiling the critical factors in crystal structure graph representation: a comparative analysis using streamlined MLPSets frameworks
Hongwei Du, Hong Wang
Graph Neural Networks have rapidly advanced in materials science and chemistry,with their performance critically dependent on comprehensive representations of crystal or molecular…
cond-mat.mtrl-sci2025
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
Hongwei Du, Jiamin Wang, Jian Hui +2
Generative models generate vast numbers of hypothetical materials, necessitating fast, accurate models for property prediction. Graph Neural Networks (GNNs) excel in this domain bu…