Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems

no code implementations • 15 Jun 2022 • Cong Fu, Xuan Zhang, Huixin Zhang, Hongyi Ling, Shenglong Xu, Shuiwang Ji

Based on the proposed lattice convolutions, we design lattice convolutional networks (LCN) that use self-gating and attention mechanisms.

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks

1 code implementation • NeurIPS Workshop AI4Scien 2021 • Meng Liu, Cong Fu, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, Shuiwang Ji

We employ our methods to participate in the 2021 KDD Cup on OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy gap of molecules.

Molecular Property Prediction

Hilbert Space Fragmentation and Exact Scars of Generalized Fredkin Spin Chains

no code implementations • 11 Feb 2021 • Christopher M. Langlett, Shenglong Xu

In this work, based on the Fredkin spin chain, we introduce a family of spin-$1/2$ many-body Hamiltonians with a three-site interaction featuring a fragmented Hilbert space with coexisting quantum many-body scars.

Strongly Correlated Electrons Quantum Physics