Search Results for author: Jan Hermann

Found 5 papers, 3 papers with code

Ab-initio quantum chemistry with neural-network wavefunctions

no code implementations26 Aug 2022 Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo, W. M. C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé

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.

Quantization

Electronic excited states in deep variational Monte Carlo

no code implementations17 Mar 2022 Mike Entwistle, Zeno Schätzle, Paolo A. Erdman, Jan Hermann, Frank Noé

Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications.

Variational Monte Carlo

Convergence to the fixed-node limit in deep variational Monte Carlo

1 code implementation11 Oct 2020 Zeno Schätzle, Jan Hermann, Frank Noé

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.

Variational Monte Carlo

Deep-neural-network solution of the electronic Schrödinger equation

2 code implementations Nature Chemistry 2020 Jan Hermann, Zeno Schätzle, Frank Noé

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.

valid

Deep neural network solution of the electronic Schrödinger equation

1 code implementation16 Sep 2019 Jan Hermann, Zeno Schätzle, Frank Noé

The electronic Schr\"odinger equation describes fundamental properties of molecules and materials, but can only be solved analytically for the hydrogen atom.

valid

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