Teaching a Neural Network Quantum Mechanics. A Deep Learning Approach to the N-Representability Problem.
(2018) FYSM60 20181Mathematical 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.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8951887
- author
- Johansson, Emil LU
- supervisor
-
- Mats-Erik Pistol LU
- Gillis Carlsson LU
- Najmeh Abiri LU
- organization
- course
- FYSM60 20181
- year
- 2018
- 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}}, }