4 papers
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…
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…
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…
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…