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Deep learning for inverse problems in quantum mechanics
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- Lantz, Victor (author)
- Lund University
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- Abiri, Najmeh (author)
- Lund University,Lunds universitet,Beräkningsbiologi och biologisk fysik - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Computational Biology and Biological Physics - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science
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- Carlsson, Gillis (author)
- Lund University,Lunds universitet,Matematisk fysik,Fysiska institutionen,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Physics,Department of Physics,Departments at LTH,Faculty of Engineering, LTH
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- (creator_code:org_t)
- 2020-12-31
- 2021
- English.
- In: International Journal of Quantum Chemistry. - : Wiley. - 0020-7608 .- 1097-461X. ; 121:9
- Journal article (peer-reviewed)
Abstract
Subject headings
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- Inverse problems are important in quantum mechanics and involve such questions as finding which potential give a certain spectrum or which arrangement of atoms give certain properties to a molecule or solid. Inverse problems are typically very hard to solve and tend to be very compute intense. We here show that neural networks can easily solve inverse problems in quantum mechanics. It is known that a neural network can compute the spectrum of a given potential, a result which we reproduce. We find that the (much harder) inverse problem of computing the correct potential that gives a prescribed spectrum is equally easy for a neural network. We extend previous work where neural networks were used to find the electronic many-particle density given a potential by considering the inverse problem. That is, we show that neural networks can compute the potential that gives a prescribed many-electron density.
Subject headings
- NATURVETENSKAP -- Kemi -- Teoretisk kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences -- Theoretical Chemistry (hsv//eng)
- NATURVETENSKAP -- Fysik -- Den kondenserade materiens fysik (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences -- Condensed Matter Physics (hsv//eng)
Keyword
- deep learning
- density functional theory
- inverse problems
- quantum mechanics
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- art (subject category)
- ref (subject category)
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