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Andrés I Bertoni1, Cristián G Sánchez1
1Instituto Interdisciplinario de Ciencias Básicas (ICB-CONICET), Universidad Nacional de Cuyo, Padre Jorge Contreras 1300, Mendoza 5502, Argentina. csanchez@mendoza-conicet.gob.ar.
This study benchmarks approximate density-functional tight-binding (DFTB) excited state (ES) methods using machine learning data. Findings reveal prediction errors strongly depend on chemical identity, offering insights for improving DFTB ES calculations.
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