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

## TL;DR

This paper uses supervised machine learning with entanglement spectrum data to accurately map the phase diagram of the half-filled one-dimensional extended Hubbard model, confirming the stability of the bond-order-wave phase.

## Contribution

It introduces a novel approach combining ML and DMRG to analyze phase stability in a complex quantum model.

## Key findings

- ML accurately identifies phase boundaries
- Bond-order-wave phase remains stable in the thermodynamic limit
- Method can be applied to other quantum many-body systems

## 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.