Machine Learning Phase Diagram in the Half-filled One-dimensional Extended Hubbard Model

Machine Learning Phase Diagram in the Half-filled One-dimensional Extended Hubbard Model

We demonstrate that supervised machine learning (ML) with entanglement spectrum can give useful information for constructing phase diagram in the half-filled one-dimensional extended Hubbard model. Combining ML with infinite-size density-matrix renormalization group, we confirm that bond-order-wave...

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Veröffentlicht in:Journal of the Physical Society of Japan 2019-06, Vol.88 (6), p.65001
Hauptverfasser: Shinjo, Kazuya, Sasaki, Kakeru, Hase, Satoru, Sota, Shigetoshi, Ejima, Satoshi, Yunoki, Seiji, Tohyama, Takami
Format: Artikel
Sprache:eng
Schlagworte:
Artificial intelligence
Entanglement
Machine learning
Phase diagrams
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Zusammenfassung:We demonstrate that supervised machine learning (ML) with entanglement spectrum can give useful information for constructing phase diagram in the half-filled one-dimensional extended Hubbard model. Combining ML with infinite-size density-matrix renormalization group, we confirm that bond-order-wave phase remains stable in the thermodynamic limit.
ISSN:0031-9015
1347-4073
DOI:10.7566/JPSJ.88.065001