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Found 8 papers, 5 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.
Accurate and scalable exchange-correlation with deep learning
Skala achieves chemical accuracy for atomization energies of small molecules while retaining the computational efficiency typical of semi-local DFT.
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.
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.
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.
