DFTB3: Extension of the self-consistent-charge density-functional tight-binding method (SCC-DFTB)

By Michael Gaus, Qiang Cui, Marcus Elstner. Journal of Chemical Theory and Computation, 2011

988 citations recorded by the source corpus.

The self-consistent-charge density-functional tight-binding method (SCC-DFTB) is an approximate quantum chemical method derived from density functional theory (DFT) based on a second-order expansion of the DFT total energy around a reference density. In the present study we combine earlier extensions and improve them consistently with, first, an improved Coulomb interaction between atomic partial charges, and second, the complete third-order expansion of the DFT total energy. These modifications lead us to the next generation of the DFTB methodology called DFTB3, which substantially improves the description of charged systems containing elements C, H, N, O, and P, especially regarding hydrogen binding energies and proton affinities. As a result, DFTB3 is particularly applicable to biomolecular systems. Remaining challenges and possible solutions are also briefly discussed.

Subfields: Atomic and Molecular Physics, and Optics, Organic Chemistry, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/128ecef89863a7bc2c0b8613a053c40e3406eb0c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Christoph Bannwarth
2. Stefan Grimme
3. Andreas Hansen
4. Sebastian Ehlert
5. Alán Aspuru-Guzik
6. Philipp Pracht
7. Eike Caldeweyher
8. Florian Hase

In Organic Chemistry

1. Christoph Bannwarth
2. Andreas Hansen
3. Alán Aspuru-Guzik
4. Stefan Grimme

In Physical and Theoretical Chemistry

1. Stefan Grimme
2. Christoph Bannwarth
3. Andreas Hansen
4. Alán Aspuru-Guzik
5. Florian Hase
6. Pascal Friederich
7. Jonny Proppe
8. Sebastian Ehlert

Citations in the corpus

• Extended tight‐binding quantum chemistry methods (2020, background): “The most sophisticated variants truncate this expansion after the third-order term.(16,48) The same is true for the GFN1-xTB(53) and GFN2-xTB(49) approaches, while GFN0-xTB corresponds to truncation after the first order term (see below).”
• Machine-learned potentials for next-generation matter simulations (2021, background): “Computationally less costly semi-empirical and tight-binding methods 119–121 , as well as quantum mechanics/molecular mechanics (QM/MM) models 115,122–124 , are a promising approach to speed up calculations that we will discuss later on in the context of ML potentials.”