3 citations · 4 across the 5 of their papers we have counts for
5 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…
InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames
Haorui Li, Weitao Du, Yuqiang Li +2
Transformer-based autoregressive models have emerged as a unifying paradigm across modalities such as text and images, but their extension to 3D molecule generation remains underex…
Flow Along the K-Amplitude for Generative Modeling
Weitao Du, Shuning Chang, Jiasheng Tang +3
In this work, we propose a novel generative learning paradigm, K-Flow, an algorithm that flows along the -amplitude. Here, is a scaling parameter that organizes frequency ba…
A quatum inspired neural network for geometric modeling
Weitao Du, Shengchao Liu, Xuecang Zhang
By conceiving physical systems as 3D many-body point clouds, geometric graph neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased promising performance. In pa…
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
Weitao Du, Jiujiu Chen, Xuecang Zhang +2
Recently, artificial intelligence for drug discovery has raised increasing interest in both machine learning and chemistry domains. The fundamental building block for drug discover…