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arXiv 1209.5735physics.chem-phcond-mat.mtrl-sciphysics.atm-clus
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非经验调谐距离分离DFT精确预测DNA和RNA核碱基中的基频和激发能隙

Non-Empirically Tuned Range-Separated DFT Accurately Predicts Both Fundamental and Excitation Gaps in DNA and RNA Nucleobases

Michael E. Foster, Bryan M. Wong

摘要:

使用非经验调谐的距离分离DFT方法,我们研究了DNA和RNA核碱基(腺嘌呤,胸腺嘧啶,胞嘧啶,鸟嘌呤和尿嘧啶)的准粒子性质(HOMO-LUMO基本间隙)和激发能。我们的计算表明,一个物理动机,第一原理调整DFT方法准确地再现了实验基准和计算密集型技术,如多体GW理论的结果。此外,在同一组的核碱基,我们表明,非经验的范围分离的程序也导致显着改善的结果相比,传统的DFT方法的激发能。本研究结果强调了一个非经验调谐范围分离方法的重要性,用于准确预测DNA和RNA核碱基的基本和激发间隙。

英文摘要:

Using a non-empirically tuned range-separated DFT approach, we study both the quasiparticle properties (HOMO-LUMO fundamental gaps) and excitation energies of DNA and RNA nucleobases (adenine, thymine, cytosine, guanine, and uracil). Our calculations demonstrate that a physically-motivated, first-principles tuned DFT approach accurately reproduces results from both experimental benchmarks and more computationally intensive techniques such as many-body GW theory. Furthermore, in the same set of nucleobases, we show that the non-empirical range-separated procedure also leads to significantly improved results for excitation energies compared to conventional DFT methods. The present results emphasize the importance of a non-empirically tuned range-separation approach for accurately predicting both fundamental and excitation gaps in DNA and RNA nucleobases.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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