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Found 3 papers, 1 papers with code
A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems.
Convergence of variational Monte Carlo simulation and scale-invariant pre-training
We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem.
Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation
We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing products of determinants with products of antisymmetrized neural networks.
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