The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
DFTB+, a software package for efficient approximate density functional theory based atomistic simulations
Open accessAbstract
DFTB+ is a versatile community developed open source software package offering fast and efficient methods for carrying out atomistic quantum mechanical simulations. By implementing various methods approximating density functional theory (DFT), such as the density functional based tight binding (DFTB) and the extended tight binding method, it enables simulations of large systems and long timescales with reasonable accuracy while being considerably faster for typical simulations than the respective ab initio methods. Based on the DFTB framework, it additionally offers approximated versions of various DFT extensions including hybrid functionals, time dependent formalism for treating excited systems, electron transport using non-equilibrium Green’s functions, and many more. DFTB+ can be used as a user-friendly standalone application in addition to being embedded into other software packages as a library or acting as a calculation-server accessed by socket communication. We give an overview of the recently developed capabilities of the DFTB+ code, demonstrating with a few use case examples, discuss the strengths and weaknesses of the various features, and also discuss on-going developments and possible future perspectives.
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Cites
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135
Effect of the damping function in dispersion corrected density functional theory
Grimme, S., Ehrlich, S., & Goerigk, L. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21759 · Cited by 28,221
Tkatchenko, A., & Scheffler, M. (2009) · Physical Review Letters · doi:10.1103/physrevlett.102.073005 · Cited by 6,286
Self-citationLaio, A., & Parrinello, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202427399 · Cited by 5,626
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00933 · Cited by 63
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Cited by
Thompson, A. P., Aktulga, H. M., Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S., in 't Veld, P. J., Kohlmeyer, A., Moore, S. G., Nguyen, T. D., et al. (2022) · Computer Physics Communications · doi:10.1016/j.cpc.2021.108171 · Cited by 12,598
Blum, V., Asahi, R., Autschbach, J., Bannwarth, C., Bihlmayer, G., Blügel, S., Burns, L. A., Crawford, T. D., Dawson, W., de Jong, W. A., et al. (2024) · Electronic Structure · doi:10.1088/2516-1075/ad48ec · Cited by 24
Self-citationActive learning of neural network potentials for rare events
(2024) · doi:10.1039/d3dd00216k · Cited by 20
Villard, J., Kılıç, M., & Rothlisberger, U. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01078 · Cited by 19
Metadata
- Authors
- Hourahine, B., Aradi, B., Blum, V., Bonafé, F., Buccheri, A., Camacho, C., Cevallos, C., Deshaye, M. Y., Dumitrică, T., Dominguez, A., Ehlert, S., Elstner, M., van der Heide, T., Hermann, J., Irle, S., Kranz, J. J., Köhler, C., Kowalczyk, T., Kubař, T., Lee, I. S., Lutsker, V., Maurer, R. J., Min, S. K., Mitchell, I., Negre, C., Niehaus, T. A., Niklasson, A. M. N., Page, A. J., Pecchia, A., Penazzi, G., Persson, M. P., Řezáč, J., Sánchez, C. G., Sternberg, M., Stöhr, M., Stuckenberg, F., Tkatchenko, A., Yu, V. W.-Z., & Frauenheim, T.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2020-03-23
- Volume
- 152
- Issue
- 12
- Pages
- 124101-124101
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W3013002235
- Cited by
- 1,180
- References
- 6
- FWCI
- 60.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
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