Spin parameter optimization for spin‐polarized extended tight‐binding methods
We present an optimization strategy for atom‐specific spin‐polarization constants within the spin‐polarized GFN2‐xTB framework, aiming to enhance the accuracy of molecular simulations. We compare a sequential and global optimization of spin parameters for hydrogen, carbon, nitrogen, oxygen, and fluo...
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Veröffentlicht in: | Journal of computational chemistry 2024-12, Vol.45 (32), p.2786-2792 |
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Sprache: | eng |
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Zusammenfassung: | We present an optimization strategy for atom‐specific spin‐polarization constants within the spin‐polarized GFN2‐xTB framework, aiming to enhance the accuracy of molecular simulations. We compare a sequential and global optimization of spin parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine. Sensitivity analysis using Sobol indices guides the identification of the most influential parameters for a given reference dataset, allowing for a nuanced understanding of their impact on diverse molecular properties. In the case of the W4‐11 dataset, substantial error reduction was achieved, demonstrating the potential of the optimization. Transferability of the optimized spin‐polarization constants over different properties, however, is limited, as we demonstrate by applying the optimized parameters on a set of singlet‐triplet gaps in carbenes. Further studies on ionization potentials and electron affinities highlight some inherent limitations of current extended tight‐binding methods that can not be resolved by simple parameter optimization. We conclude that the significantly improved accuracy strongly encourages the present re‐optimization of the spin‐polarization constants, whereas the limited transferability motivates a property‐specific optimization strategy.
We demonstrate how the optimization of spin‐polarization parameters strongly improves the accuracy of extended tight‐binding methods. Further, we introduce an optimization strategy based on a sensitivity analysis for an efficient system‐ or property‐dependent parameter optimization. |
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ISSN: | 0192-8651 1096-987X 1096-987X |
DOI: | 10.1002/jcc.27482 |