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.
Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo
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abstract
Inspired by the universal approximation theorem and widespread adoption of artificial neural network techniques in a diversity of fields, we propose feed-forward neural networks as a general purpose trial wave function for quantum Monte Carlo simulations of continous many-body systems. Whereas for simple model systems the whole many-body wave function can be represented by a neural network, the antisymmetry condition of non-trivial fermionic systems is incorporated by means of a Slater determinant. To demonstrate the accuracy of our trial wave functions, we have studied an exactly solvable model system of two trapped interacting particles, as well as the hydrogen dimer.
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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.