3 papers
cond-mat.mtrl-sci2026
Predicting Atomistic Transitions with Transformers
Henry Tischler, Wenting Li, Qi Tang +2
Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation technique…
cs.LG2025
Equivariant Spherical Transformer for Efficient Molecular Modeling
Junyi An, Xinyu Lu, Chao Qu +6
Equivariant Graph Neural Networks (GNNs) have significantly advanced the modeling of 3D molecular structure by leveraging group representations. However, their message passing, hea…
cs.LG2025
Equivariant Masked Position Prediction for Efficient Molecular Representation
Junyi An, Chao Qu, Yun-Fei Shi +4
Graph neural networks (GNNs) have shown considerable promise in computational chemistry. However, the limited availability of molecular data raises concerns regarding GNNs' ability…