Dr Lars Goerigk
Melbourne Centre for Theoretical and Computational Chemistry,
School of Chemistry, The University of Melbourne, VIC 3010, Australia
Video Recording
Video is available only for registered users.
Abstract
The importance of Density Functional Theory (DFT) to the chemical sciences is well known. However, despite today’s easy access to DFT software packages to everyone, there remains a large communication gap between DFT developers and users that has resulted in various misconceptions and the regular application of outdated procedures. One reason for this is the fact that there is not only one manifestation of DFT, but hundreds of approximations to the true, unknown functional. Naturally, not only users that are new to the field, but also experts can find this ever-growing `zoo’ of DFT approximations confusing.
This presentation provides an overview of the zoo of DFT methods and is suitable to both students that are new to the field and more experienced researchers. Rather than spending too much time on discussing the physical/mathematical foundation of DFT, I will focus on aspects that are relevant to computational applications with guidelines and recommendations as take-home messages that may assist in future research endeavours. After a short overview of the basic idea of DFT and Perdew’s famous Jacob’s Ladder classification of DFT approximations, I will cover how we can identify the best and most robust representatives of the zoo for applications to thermochemistry and kinetics. A special emphasis will be given to the importance of London-dispersion interactions. Towards the end, more specialised aspects will be discussed that may be of interest to more theoretically oriented viewers.
Free-access overview that summarises most parts of this presentation:
1) L. Goerigk, N. Mehta, Aust. J. Chem. 2019, 72, 563, http://www.publish.csiro.au/CH/CH19023
Large-scale benchmarking (original research or reviews):
2) Open-Access: L. Goerigk, A. Hansen, C. Bauer, S. Ehrlich, A. Najibi, S. Grimme, PCCP 2017, 19, 32184, https://pubs.rsc.org/en/Content/ArticleLanding/2017/CP/C7CP04913G
3) N. Mehta, M. Casanova-Páez, L. Goerigk, PCCP 2018, 20, 23175, https://pubs.rsc.org/en/content/articlelanding/2014/CP/C8CP03852J
4) A. Najibi, L. Goerigk, JCTC 2018, 14, 5725, https://pubs.acs.org/doi/10.1021/acs.jctc.8b00842
5) Open-Access: N. Mardirossian, M. Head-Gordon, Mol. Phys. 2017, 115, 2315, https://www.tandfonline.com/doi/full/10.1080/00268976.2017.1333644
6) Open-Access: J. M. L. Martin, G. Santra, Isr. J. Chem. 2019, in print, https://onlinelibrary.wiley.com/doi/10.1002/ijch.201900114
7) Link to more information on the GMTKN55 database: https://goerigk.chemistry.unimelb.edu.au/research/the-gmtkn55-database/
Virtual Winter School on Computational Chemistry