Towards the design of molecular materials: from many-body methods to enhanced density functional approximations

New technologies are made possible by new materials, and until recently new materials could only be discovered experimentally. However, approaches based on the fundamental laws of quantum mechanics are now integrated to many design initiatives in academia and industry, underpinning efforts such as the Materials Genome initiative or the computational crystal structure prediction (CSP [1]). The latest CSP blind test organized by the Cambridge Crystallographic Data Center [2] revealed two major remaining challenges:
(i) Crystal polymorphs are often separated by just a few kJ/mol, exceeding the accuracy of standard density functional approximations (DFAs).
(ii) Dealing with a vast search space, in particular for molecules with increased flexibility, one has to sample about 1 Mio possible crystal structures.
Recent algorithmic developments in Quantum Monte-Carlo make it feasible to molecular crystals and we are now able to predict static lattice energies with potentially sub-chemical accuracy [3]. On the other hand, cost-effective electronic structure methods will be presented that gain up to four orders of magnitude in computational speed compared to traditional DFAs and are suited for optimizing a huge number of putative crystal structures [4]. Promising applications to the CSP of pharmaceutical-like molecules have been demonstrated recently [5]. A perspective on employing machine learning techniques in the CSP context will be discussed.

[1] S. L. Price, JGB, Molecular Crystal Structure Prediction; Elsevier Australia, 2017.
[2] A. M. Reilly, R. I. Cooper, C. S. Adjiman, S. Bhattacharya, A. D. Boese, JGB, P. J. Bygrave, R. Bylsma, J.E. Campbell, R. Car, et al. Acta. Cryst. B 2016, 72, 439.
[3] A. Zen, JGB, J. Klimeš, A. Tkatchenko, D. Alfè, A. Michaelides, Proc. Natl. Acad. Sci. USA 2018, 115, 1724.
[4] E. Caldeweyher, JGB, J. Phys.: Condens. Matter 2018, 30, 213001.
[5] L. Iuzzolino, P. McCabe, S. L. Price, JGB, Faraday Discuss. 2018, 211, 275.

About the speaker — Following his Diplom in physics at Heidelberg University, Dr. Brandenburg completed his dissertation in Theoretical Chemistry in 2015. He moved to the University College London as a visiting lecturer funded by the Alexander von Humboldt foundation. In 2018, he moved back to Germany, where he currently continues his research at the University of Göttingen. His research involves computer simulations of molecular crystals with specific focus on the prediction of organic crystal structures and their properties. He develops and applies simplified density functional based electronic structure approaches as well as many-body methodologies. Dr. Brandenburg has been awarded numerous early career prices, among them the PhD price of the university society Bonn for the best thesis over all disciplines. His research has been published in over 40 peer-reviewed articles. He is partner of the ERC consortium NanoSolveIT and contributor of an INCITE 2019 project funded by the U.S. Department of Energy.