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

TL;DR

This paper demonstrates that a non-empirically tuned range-separated DFT method can accurately predict both fundamental and excitation gaps in DNA and RNA nucleobases, aligning well with experimental and advanced theoretical results.

Contribution

The study introduces a physically-motivated, first-principles tuned DFT approach that improves the accuracy of gap and excitation energy predictions in nucleobases without empirical parameters.

Findings

Accurately reproduces experimental and GW benchmark results for fundamental gaps.

Significantly improves excitation energy predictions over conventional DFT.

Highlights the importance of non-empirical tuning for reliable electronic property calculations.

Abstract

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.