1 citations · 1 across the 4 of their papers we have counts for
4 papers
Through the Looking-Glass: Efficient Parity-Complete Learning via Local Frames
Zemin Xu, Peijun Hu, Wenbo Xie
Making all symmetry-allowed tensor representations computationally accessible remains a central challenge in equivariant atomistic learning. Clebsch--Gordan tensor products (CGTPs)…
Edge Cluster Expansion with Radial Rotary Attention for Interatomic Potentials
Zemin Xu, Wenbo Xie, P. Hu
In this paper, we provide a systematic investigation of SO(2) theory to machine learning interatomic potentials (MLIPs) and identify the limitations of conventional SO(2) Linear ar…
A Cartesian-3j Framework for Machine Learning Interatomic Potentials
Zemin Xu, Chenyu Wu, Wenbo Xie +1
Machine learning interatomic potentials (MLIPs) have brought substantial gains in the extrapolation capability in computational chemistry. However, most equivariant models are typi…
Spectral/Spatial Tensor Atomic Cluster Expansion with Universal Embeddings in Cartesian Space
Zemin Xu, Wenbo Xie, P. Hu
Equivariant atomistic machine learning models have largely been built on spherical-tensor representations, where explicit angular-momentum coupling introduces substantial complexit…