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arXiv · 2511.12551

Principal Component Analysis of Competing Correlations in Quarter-Filled Hubbard Models

Md Fahad Equbal·S R Hassan·M. A. H. Ahsan
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Abstract

We present an unsupervised learning analysis of correlation hierarchies in the quarter-filled simple and extended Hubbard models by applying principal component analysis (PCA) to exact-diagonalization (ED) data on 3x4 and 4x4 cylindrical clusters. While the non-interacting limit (U=0) provides a finite-size reference, increasing on-site repulsion U induces localization and reorganizes the low-energy spectrum. For the extended model, we examine moderate (U=4) and strong (U=10) coupling regimes, where conventional structure factors reveal familiar crossovers among charge, spin and local-pairing correlations. PCA of the corresponding correlation matrices captures these crossovers directly from the data, without assuming predefined order parameters by identifying charge-dominated, spin-dominated and pairing-dominated regimes through variance condensation into leading components. This establishes PCA as a transparent, model-agnostic framework for uncovering the hierarchy and competition of correlation channels in finite Hubbard clusters, providing a bridge between exact diagonalization and modern machine-learning diagnostics in strongly correlated systems.

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Md Fahad Equbal, S R Hassan, M. A. H. Ahsan. 2025-11-16. Principal Component Analysis of Competing Correlations in Quarter-Filled Hubbard Models. https://arxiv.org/abs/2511.12551

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