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

Abstract

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.

2 pages, 2 figures

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