Monday, March 9, 2015

Large-scale virtual high-throughput screening for the identification of new battery electrolyte solvents: computing infrastructure and collective properties

Tamara Husch, Nusret Duygu Yilmazer, Andrea Balducci and Martin Korth, Phys. Chem. Chem. Phys., 2015,17, 3394-3401
Contributed by Tobias Schwabe

If you are among those who (like me) follow from time to time what is going on in volunteer computing for Computational Chemistry, you might be well aware of the first projects in this field: QMC@home (see: www.qmcathome.org). It's been a little bit quiet around there lately but now it seems Martin Korth and his team are setting the stage for a new project for the QMC@home community: cleanmobility.now. The foundations for that are laid out in their recent PCCP paper.

Cleanmobility.now aims at supporting the development of better batteries for electric cars. As a first starting point, finding new electrolytes has been chosen as target – certainly a good choice as this idea is quite in vogue right now: e.g. see this JPC/C Feature Article (which, by the way, also shows the usage of the very interesting projected WFT-in-DFT embedding method to get accurate results for large complex systems)[1]. And for a more general overview, you can check some recent reviews about Computational Chemistry in this field [2,3].

Husch et al. make an interesting contribution here because they attack the problem by virtual HTS, one of the few studies where this idea is not “just” applied for drug discovery. Especially, they tackle the problem of collective properties. Their pilot studies show already some promising results. For example, they found nitriles to be potential electrolytes, which have also attracted some interest from experimental side.

References

[1] Taylor A. Barnes, Jakub W. Kaminski, Oleg Borodin, and Thomas F. Miller, III, J. Phys. Chem. C 2015, 119, 3865−3880, DOI: 10.1021/jp510882g

[2] Mahesh Datt Bhatt and Colm O’Dwyer, Phys. Chem. Chem. Phys., 2015, 17, 4799—4844, DOI: 10.1039/c4cp05552g

[3] Martin Korth, in Specialist Periodical Reports: Chemical Modeling: Applications and Theory, ed. M. Springborg and J.-O. Joswig, Royal Society of Chemistry, London, UK, 2014