The Fermionic Neural Network is an antisymmetric neural-network wavefunction which, optimized variationally, recovers most correlation energy and outperforms CCSD(T) on several strongly correlated dissociation curves.
Solving Many-Electron Schr\"odinger Equation Using Deep Neural Networks
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abstract
We introduce a new family of trial wave-functions based on deep neural networks to solve the many-electron Schr\"odinger equation. The Pauli exclusion principle is dealt with explicitly to ensure that the trial wave-functions are physical. The optimal trial wave-function is obtained through variational Monte Carlo and the computational cost scales quadratically with the number of electrons. The algorithm does not make use of any prior knowledge such as atomic orbitals. Yet it is able to represent accurately the ground-states of the tested systems, including He, H2, Be, B, LiH, and a chain of 10 hydrogen atoms. This opens up new possibilities for solving large-scale many-electron Schr\"odinger equation.
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physics.chem-ph 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Ab-Initio Solution of the Many-Electron Schr\"odinger Equation with Deep Neural Networks
The Fermionic Neural Network is an antisymmetric neural-network wavefunction which, optimized variationally, recovers most correlation energy and outperforms CCSD(T) on several strongly correlated dissociation curves.