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Found 6 papers, 4 papers with code
Neural Wave Functions for Superfluids
Understanding superfluidity remains a major goal of condensed matter physics.
Ab-initio quantum chemistry with neural-network wavefunctions
Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery.
Discovering Quantum Phase Transitions with Fermionic Neural Networks
Deep neural networks have been extremely successful as highly accurate wave function ans\"atze for variational Monte Carlo calculations of molecular ground states.
Better, Faster Fermionic Neural Networks
The Fermionic Neural Network (FermiNet) is a recently-developed neural network architecture that can be used as a wavefunction Ansatz for many-electron systems, and has already demonstrated high accuracy on small systems.
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
Here we introduce a novel deep learning architecture, the Fermionic Neural Network, as a powerful wavefunction Ansatz for many-electron systems.
The HANDE-QMC project: open-source stochastic quantum chemistry from the ground state up
Building on the success of Quantum Monte Carlo techniques such as diffusion Monte Carlo, alternative stochastic approaches to solve electronic structure problems have emerged over the last decade.
Computational Physics
