AL-ATCI uses active learning to identify the relevant determinant manifold in configuration-interaction impurity solvers, achieving weak scaling with bath size and reproducing exact-diagonalization accuracy for Hubbard model clusters up to size 10 and Sr2RuO4 impurities.
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cond-mat.str-el 2years
2026 2verdicts
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A new algorithm applies Hubbard U corrections to electron-phonon g matrices via finite-displacement DFT+U, applied to 20% hole-doped LaNiO2 and strained RuO2, finding modest coupling increase insufficient for observed Tc in nickelates but stabilization and reduced coupling in ruthenates.
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A Scalable Configuration-Interaction Impurity Solver via Active Learning
AL-ATCI uses active learning to identify the relevant determinant manifold in configuration-interaction impurity solvers, achieving weak scaling with bath size and reproducing exact-diagonalization accuracy for Hubbard model clusters up to size 10 and Sr2RuO4 impurities.
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Hubbard-$U$-corrected electron-phonon interactions in strongly correlated materials via the finite-displacement method
A new algorithm applies Hubbard U corrections to electron-phonon g matrices via finite-displacement DFT+U, applied to 20% hole-doped LaNiO2 and strained RuO2, finding modest coupling increase insufficient for observed Tc in nickelates but stabilization and reduced coupling in ruthenates.