3 papers
cond-mat.mtrl-sci2026
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…
cs.LG2025
Molecule Graph Networks with Many-body Equivariant Interactions
Zetian Mao, Chuan-Shen Hu, Jiawen Li +5
Message passing neural networks have demonstrated significant efficacy in predicting molecular interactions. Introducing equivariant vectorial representations augments expressivity…
cond-mat.mtrl-sci2025
CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines
Chen Liang, Diptesh Das, Jiang Guo +3
Solving black-box optimization problems with Ising machines is increasingly common in materials science. However, their application to crystal structure prediction (CSP) is still i…