36 citations · 37 across the 4 of their papers we have counts for
5 papers
Understanding compression-extension error asymmetry in universal machine-learning interatomic potentials
Hongwei Du, Dingyang Lv, Boyan Zhang +11
Universal machine-learning interatomic potentials are increasingly used across diverse materials, yet their reliability under directional deformation remains unclear. We compare co…
A benchmark dataset and baseline methods for four-dimensional STEM diffraction patterns
Yuyan Guan, Haoran Zhang, Zian Mao +7
Four-dimensional scanning transmission electron microscopy (4D-STEM) records a two-dimensional diffraction pattern at each electron-probe position, yielding spatially resolved reci…
A Hierarchical Synergistic Deep Learning Framework Integrating Composition, Structure, and Ionic Transport for Solid-State Electrolyte Discovery
Hongwei Du, Dingyang Lv, Baole Wei +5
Inorganic solid-state electrolytes must combine high room-temperature ionic conductivity, a wide electrochemical window, excellent electronic insulation, and favorable mechanical c…
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…
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…