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

delete2024-08-22
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OA
AI
S
Siyavash Moradi
R
Rebecca Tomann
J
Josie Hendrix
M
Martin Head‐Gordon
C
Christopher J. Stein *
DOI:10.1002/jcc.27482delete
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摘要

摘要

En 中文
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
Keyword:
benchmark
density functional tight-binding
parameter optimization
semi-empirical methods
sensitivity analysis
spin-polarization

期刊

Journal of Computational Chemistry 封面图
Journal of Computational Chemistry
IF:
4.8
论文数:
7.1K
被引数:
6.1W

机构

University of California System 封面图
University of California System
学者数:
37.7W
论文数: 33.8W
被引数: 6.6K
T
Technical University of Munich
学者数:
5.2W
论文数: 3.9W
被引数: 6.2W
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