most citedDenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

36 citations · 37 across the 2 of their papers we have counts for

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cond-mat.mtrl-sci2026

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

Bin Cao, Jie Xiong, Jiaxuan Ma +11

Efficient exploration of vast compositional and processing spaces remains a major challenge in accelerated materials discovery. Bayesian optimization (BO) provides a principled app…

cond-mat.mtrl-sci2025★ 1 cited

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors

Hongwei Du, Jian Hui, Lanting Zhang +1

With the rapid development of energy storage technology, high-performance solid-state electrolytes (SSEs) have become critical for next-generation lithium-ion batteries. These mate…

cond-mat.mtrl-sci2025★ 36 cited

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…