Phase diagram study of a two-dimensional frustrated antiferromagnet via unsupervised machine learning
We apply unsupervised learning techniques to classify the different phases of the J₁-J₂ antiferromagnetic Ising model on the honeycomb lattice. We construct the phase diagram of the system using convolutional autoencoders. These neural networks can detect phase transitions in the system via "an...
Guardado en:
| Autores principales: | Acevedo, Santiago Daniel, Arlego, Marcelo José Fabián, Lamas, Carlos Alberto |
|---|---|
| Formato: | Articulo Preprint |
| Lenguaje: | Inglés |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/125201 |
| Aporte de: |
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