Spin parameter optimization for spin-polarized extended tight-binding methods

被引:1
作者
Moradi, Siyavash [1 ]
Tomann, Rebecca [2 ]
Hendrix, Josie [2 ]
Head-Gordon, Martin [2 ]
Stein, Christopher J. [1 ]
机构
[1] Tech Univ Munich, Res Ctr, TUM Sch Nat Sci & Catalysis, Dept Chem, Lichtenbergstr 4, D-85748 Garching, Germany
[2] Univ Calif Berkeley, Pitzer Ctr Theoret Chem, Dept Chem, Berkeley, CA USA
关键词
benchmark; density functional tight-binding; parameter optimization; semi-empirical methods; sensitivity analysis; spin-polarization; DENSITY-FUNCTIONAL THEORY; SENSITIVITY-ANALYSIS; BENCHMARK DATASET; THERMOCHEMISTRY; SIMULATIONS; KINETICS; DFTB; TOOL;
D O I
10.1002/jcc.27482
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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. image
引用
收藏
页码:2786 / 2792
页数:7
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