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Teaching a Neural Network Quantum Mechanics. A Deep Learning Approach to the N-Representability Problem.

Johansson, Emil LU (2018) FYSM60 20181
Mathematical Physics
Department of Physics
Solid State Physics
Abstract
We show that an unsupervised artificial neural network can be trained to parameterize the set of N representable density matrices well enough to enable ground state energy calculations. A one-dimensional harmonic oscillator system is used to test the method. 4, 5, or 6 fermions are placed in an external potential. They interact with one of three different interaction types. By choosing the most successful network according to a well-defined measure, the approach is shown to generalize to interaction types not considered by the measure.
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author
Johansson, Emil LU
supervisor
organization
course
FYSM60 20181
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
8951887
date added to LUP
2018-06-21 14:51:31
date last changed
2018-06-21 14:51:31
@misc{8951887,
  abstract     = {We show that an unsupervised artificial neural network can be trained to parameterize the set of N representable density matrices well enough to enable ground state energy calculations. A one-dimensional harmonic oscillator system is used to test the method. 4, 5, or 6 fermions are placed in an external potential. They interact with one of three different interaction types. By choosing the most successful network according to a well-defined measure, the approach is shown to generalize to interaction types not considered by the measure.},
  author       = {Johansson, Emil},
  language     = {eng},
  note         = {Student Paper},
  title        = {Teaching a Neural Network Quantum Mechanics. A Deep Learning Approach to the N-Representability Problem.},
  year         = {2018},
}