<p>We investigate a one-dimensional tight-binding model in which onsite potentials <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\{\varepsilon _i\}\)</EquationSource> </InlineEquation> exhibit power-law spatial correlations (with exponent <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\alpha\)</EquationSource> </InlineEquation>) and the hopping amplitudes decay as <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(t_{ij}\sim |i-j|^{-\beta }\)</EquationSource> </InlineEquation>. This two-parameter family interpolates continuously between short-range Anderson-like disorder, correlated disorder with conventional hopping, and long-range hopping models with nontrivial delocalization tendencies. Using large-scale exact diagonalization, we construct a comprehensive phase map in the <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\((\alpha ,\beta )\)</EquationSource> </InlineEquation> plane by combining spectral statistics, density-of-states analysis, and energy-resolved localization indicators such as the participation ratio, single-particle entanglement entropy, level-spacing ratio <i>r</i>, and the ratio of the geometric to arithmetic density of states. From these observables we define phase-indicator functions that compactly quantify localization behavior across the spectrum. Our analysis reveals robust mobility edges and multiple regimes of spectral coexistence between localized, extended, resonant, and critical states. Finite-size scaling, implemented via an explicit smoothness-based cost function, enables extraction of critical exponents and delineation of transition lines across the <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\((\alpha ,\beta )\)</EquationSource> </InlineEquation> parameter space. To validate and complement these physics-based diagnostics, we employ a supervised autoencoder that learns high-level representations of eigenstate structure directly from raw features and reliably reproduces the phase classification defined by the indicator functions. Together, these approaches provide a coherent and internally consistent picture of the spectral transitions driven by correlated disorder and long-range hopping, establishing a unified framework for characterizing mobility edges in long-range one-dimensional systems.</p>

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Phase structure and machine learning identification in one dimensional systems with power law correlated disorder and long range hopping

  • Mohammad Pouranvari

摘要

We investigate a one-dimensional tight-binding model in which onsite potentials \(\{\varepsilon _i\}\) exhibit power-law spatial correlations (with exponent \(\alpha\) ) and the hopping amplitudes decay as \(t_{ij}\sim |i-j|^{-\beta }\) . This two-parameter family interpolates continuously between short-range Anderson-like disorder, correlated disorder with conventional hopping, and long-range hopping models with nontrivial delocalization tendencies. Using large-scale exact diagonalization, we construct a comprehensive phase map in the \((\alpha ,\beta )\) plane by combining spectral statistics, density-of-states analysis, and energy-resolved localization indicators such as the participation ratio, single-particle entanglement entropy, level-spacing ratio r, and the ratio of the geometric to arithmetic density of states. From these observables we define phase-indicator functions that compactly quantify localization behavior across the spectrum. Our analysis reveals robust mobility edges and multiple regimes of spectral coexistence between localized, extended, resonant, and critical states. Finite-size scaling, implemented via an explicit smoothness-based cost function, enables extraction of critical exponents and delineation of transition lines across the \((\alpha ,\beta )\) parameter space. To validate and complement these physics-based diagnostics, we employ a supervised autoencoder that learns high-level representations of eigenstate structure directly from raw features and reliably reproduces the phase classification defined by the indicator functions. Together, these approaches provide a coherent and internally consistent picture of the spectral transitions driven by correlated disorder and long-range hopping, establishing a unified framework for characterizing mobility edges in long-range one-dimensional systems.