9 citations · 16 across the 4 of their papers we have counts for
4 papers
Uncovering Neural Scaling Laws in Molecular Representation Learning
Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang +5
Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While th…
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Weitao Du, Yuanqi Du, Limei Wang +5
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks (GNNs), a co…
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction
Junwen Bai, Yuanqi Du, Yingheng Wang +3
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property…
Graph-based Ensemble Machine Learning for Student Performance Prediction
Yinkai Wang, Aowei Ding, Kaiyi Guan +2
Student performance prediction is a critical research problem to understand the students' needs, present proper learning opportunities/resources, and develop the teaching quality.…