Simulating the fractional quantum Hall effect with neural network variational Monte Carlo.
Y. Qian, T. Zhao, J. Zhang, T. Xiang, X. Li, and J. Chen, Taming Landau Level Mixing in Fractional Quantum Hall States with Deep Learning, arXiv:2412.14795.
M. Cheng, C. Wang, C. Qin, Y. Zhang, Q. Zhang, H. Li, and J. Chen, Predicting Macroscopic Properties of Amorphous Monolayer Carbon via Pair Correlation Function, arXiv:2410.03116.
Calculate observables from neural network-based VMC (NN-VMC).
Y. Qian, X. Li, and J. Chen, Force and Stress Calculation with Neural Network Wavefunction for Solids, Faraday Discuss. (2024).
Efficient and Accurate Neural-Network Ansatz for Quantum Monte Carlo.
R. Li et al., A Computational Framework for Neural Network-Based Variational Monte Carlo with Forward Laplacian, Nat Mach Intell 6, 209 (2024).
JAX accelerated Quantum Monte Carlo.
W. Ren, W. Fu, X. Wu, and J. Chen, Towards the Ground State of Molecules via Diffusion Monte Carlo on Neural Networks, Nat Commun 14, 1 (2023).
A library combining solid quantum Monte Carlo and neural network.
X. Li, Z. Li, and J. Chen, Ab Initio Calculation of Real Solids via Neural Network Ansatz, Nat Commun 13, 1 (2022).
An implementation combining FermiNet with effective core potential (ECP).
X. Li, C. Fan, W. Ren, and J. Chen, Fermionic Neural Network with Effective Core Potential, Phys. Rev. Research 4, 013021 (2022).