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SUMMARY:Towards the design of molecular materials: from many-body methods 
 to enhanced density functional approximations
DTSTART:20190124T170000
DTEND:20190124T180000
DTSTAMP:20260506T020330Z
UID:07c7f90e2dedb55f9457e8213d8407a38ea5782e233b5baaf1425126
CATEGORIES:Conferences - Seminars
DESCRIPTION:Jan Gerit Brandenburg (University of Göttingen & University C
 ollege London)\nNew technologies are made possible by new materials\, and 
 until recently new materials could only be discovered experimentally. Howe
 ver\, approaches based on the fundamental laws of quantum mechanics are no
 w integrated to many design initiatives in academia and industry\, underpi
 nning efforts such as the Materials Genome initiative or the computational
  crystal structure prediction (CSP [1]). The latest CSP blind test organiz
 ed by the Cambridge Crystallographic Data Center [2] revealed two major re
 maining challenges:\n(i) Crystal polymorphs are often separated by just a 
 few kJ/mol\, exceeding the accuracy of standard density functional approxi
 mations (DFAs).\n(ii) Dealing with a vast search space\, in particular for
  molecules with increased flexibility\, one has to sample about 1 Mio poss
 ible crystal structures.\nRecent algorithmic developments in Quantum Monte
 -Carlo make it feasible to molecular crystals and we are now able to predi
 ct static lattice energies with potentially sub-chemical accuracy [3]. On 
 the other hand\, cost-effective electronic structure methods will be prese
 nted that gain up to four orders of magnitude in computational speed compa
 red to traditional DFAs and are suited for optimizing a huge number of put
 ative crystal structures [4]. Promising applications to the CSP of pharmac
 eutical-like molecules have been demonstrated recently [5]. A perspective 
 on employing machine learning techniques in the CSP context will be discus
 sed.\n\n[1] S. L. Price\, JGB\, Molecular Crystal Structure Prediction\; E
 lsevier Australia\, 2017.\n[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.\n[3] A. Zen\, J
 GB\, J. Klimeš\, A. Tkatchenko\, D. Alfè\, A. Michaelides\, Proc. Natl. 
 Acad. Sci. USA 2018\, 115\, 1724.\n[4] E. Caldeweyher\, JGB\, J. Phys.: Co
 ndens. Matter 2018\, 30\, 213001.\n[5] L. Iuzzolino\, P. McCabe\, S. L. Pr
 ice\, JGB\, Faraday Discuss. 2018\, 211\, 275.\n\nAbout the speaker — F
 ollowing his Diplom in physics at Heidelberg University\, Dr. Brandenburg 
 completed his dissertation in Theoretical Chemistry in 2015. He moved to t
 he University College London as a visiting lecturer funded by the Alexande
 r von Humboldt foundation. In 2018\, he moved back to Germany\, where he c
 urrently continues his research at the University of Göttingen. His resea
 rch involves computer simulations of molecular crystals with specific focu
 s on the prediction of organic crystal structures and their properties. He
  develops and applies simplified density functional based electronic struc
 ture approaches as well as many-body methodologies. Dr. Brandenburg has be
 en awarded numerous early career prices\, among them the PhD price of the 
 university society Bonn for the best thesis over all disciplines. His rese
 arch has been published in over 40 peer-reviewed articles. He is partner o
 f the ERC consortium NanoSolveIT and contributor of an INCITE 2019 project
  funded by the U.S. Department of Energy.
LOCATION:MED 2 1124 https://plan.epfl.ch/?room=MED21124
STATUS:CONFIRMED
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