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research article
Finding symmetry breaking order parameters with Euclidean neural networks
January 4, 2021
Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them." We demonstrate that symmetry equivariant neural networks uphold Curie's principle and can be used to articulate many symmetry-relevant scientific questions as simple optimization problems. We prove these properties mathematically and demonstrate them numerically by training a Euclidean symmetry equivariant neural network to learn symmetry breaking input to deform a square into a rectangle and to generate octahedra tilting patterns in perovskites.
Type
research article
Web of Science ID
WOS:000605564000008
Authors
Publication date
2021-01-04
Publisher
Published in
Volume
3
Issue
1
Article Number
L012002
Peer reviewed
REVIEWED
EPFL units
Available on Infoscience
March 26, 2021
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