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

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY Journal of Computational Chemistry Pub Date : 2024-08-22 DOI:10.1002/jcc.27482
Siyavash Moradi, Rebecca Tomann, Josie Hendrix, Martin Head-Gordon, Christopher J. Stein
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Abstract

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.

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自旋极化扩展紧密结合方法的自旋参数优化。
我们介绍了在自旋极化 GFN2-xTB 框架内对特定原子自旋极化常数的优化策略,旨在提高分子模拟的准确性。我们比较了氢、碳、氮、氧和氟自旋参数的顺序优化和全局优化。利用索博尔指数进行灵敏度分析,可以确定对给定参考数据集影响最大的参数,从而深入了解这些参数对不同分子特性的影响。就 W4-11 数据集而言,误差大幅减少,证明了优化的潜力。然而,优化后的自旋极化常数在不同性质上的可转移性是有限的,我们将优化参数应用于一组碳烯中的单线-三线隙就证明了这一点。对电离电位和电子亲和力的进一步研究凸显了当前扩展紧密结合方法的一些固有局限性,这些局限性无法通过简单的参数优化来解决。我们的结论是,精确度的显著提高有力地推动了目前对自旋极化常数的重新优化,而有限的可转移性则促使我们采取针对特定性质的优化策略。
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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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