Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo
Kessler J, Calcavecchia F, Kühne TD (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 4
Article Number: 2000269
Journal Issue: 4
Abstract
Inspired by the universal approximation theorem and widespread adoption of artificial neural network techniques in a diversity of fields, feed-forward neural networks are proposed as a general purpose trial wave function for quantum Monte Carlo simulations of continuous 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 the trial wave functions, an exactly solvable model system of two trapped interacting particles, as well as the hydrogen dimer, is studied.
Involved external institutions
Germany (DE)How to cite
APA:
Kessler, J., Calcavecchia, F., & Kühne, T.D. (2021). Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo. Advanced Theory and Simulations, 4(4). https://doi.org/10.1002/adts.202000269
MLA:
Kessler, Jan, Francesco Calcavecchia, and Thomas D. Kühne. "Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo." Advanced Theory and Simulations 4.4 (2021).
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