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PeriodicWave: Neural Network Variational Monte Carlo for Solids
This repository contains a Monte-Carlo solver to train neural-network variational wavefunction to solve continuous-space Fermi systems, which was developed out of the work [M Geier, K Nazaryan, T Zaklama, L Fu, Phys. Rev. B 112, 045119 (2025)].
We provide an optimized self-attention neural network wavefunction ansatz to capture electron correlations. Additionally, we introduce a new neural network architecture -- SlaterNet -- that implements an unrestricted Hartree-Fock solver.
Our AI-based method successfully solves the problem of moire semiconductors in two-dimensional materials. It outperforms traditional exact-diagonalization methods based on a projection to a finite number of orbitals. Thus, our approach advances the state-of-the-art in simulating quantum many-body systems from first principles.
We provide an optimized self-attention neural network wavefunction ansatz to capture electron correlations. Additionally, we introduce a new neural network architecture -- SlaterNet -- that implements an unrestricted Hartree-Fock solver.
Our AI-based method successfully solves the problem of moire semiconductors in two-dimensional materials. It outperforms traditional exact-diagonalization methods based on a projection to a finite number of orbitals. Thus, our approach advances the state-of-the-art in simulating quantum many-body systems from first principles.
Our code is open source and freely available to the community.