9 citations · 10 across the 3 of their papers we have counts for
4 papers
Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks
Haowei Hua, Chen Liang, Ding Pan +4
Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such…
Local-Global Associative Frames for Symmetry-Preserving Crystal Structure Modeling
Haowei Hua, Wanyu Lin
Crystal structures are defined by the periodic arrangement of atoms in 3D space, inherently making them equivariant to SO(3) group. A fundamental requirement for crystal property p…
Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction
Haowei Hua, Jingwen Yang, Wanyu Lin +1
Predicting the tensor properties of crystalline materials is a fundamental task in materials science. Unlike scalar property prediction, which requires invariance, tensor property…
Crystalline Material Discovery in the Era of Artificial Intelligence
Zhenzhong Wang, Haowei Hua, Wanyu Lin +2
Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy…