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Found 7 papers, 6 papers with code
An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
Reliable description of bond breaking remains a major challenge for quantum chemistry due to the multireferential character of the electronic structure in dissociating species.
Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo
The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods.
Highly Accurate Real-space Electron Densities with Neural Networks
Variational ab-initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function.
Electronic excited states in deep variational Monte Carlo
Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications.
Convergence to the fixed-node limit in deep variational Monte Carlo
Variational quantum Monte Carlo (QMC) is an ab-initio method for solving the electronic Schr\"odinger equation that is exact in principle, but limited by the flexibility of the available ansatzes in practice.
Deep-neural-network solution of the electronic Schrödinger equation
The electronic Schrödinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons.
Deep neural network solution of the electronic Schrödinger equation
The electronic Schr\"odinger equation describes fundamental properties of molecules and materials, but can only be solved analytically for the hydrogen atom.
