BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:More tricks of the trade for ML descriptions of atomistic systems 
 - Dr. Edgar Engel
DTSTART:20191125T170000Z
DTEND:20191125T173000Z
UID:TALK134872@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:Using the example of a ML model for NMR chemical shieldings fo
 r molecular crystals from the CSD [1\,2]\, various tricks of the trade are
  introduced. \nIncluding (i) the efficient estimation of uncertainties [3]
 \, (ii) sparsification of the chemical space [4]\, features\, and similari
 ty kernels [5] underlying KRR models\, and (iii) the prediction of tensori
 al properties [6]\, these permit rendering comparatively simple KRR models
  (as outlined in previous talks in the MLDG series) practical and accurate
  for complex atomistic systems. \nThe example of NMR chemical shieldings w
 ill also serve to touch upon some of the key limitations of our current ML
  approaches.\n\n\n[1] Paruzzo et al.\, Nat Comm\, 9\, 4501 (2018) \n[2] En
 gel et al.\, PCCP\, 21\, 23385 (2019)\n[3] Musil et al.\, JCTC\, 15\, 906 
 (2019)\n[4] Willatt\, Musil\, and Ceriotti\, PCCP\, 20\, 29661 (2018)\n[5]
  Rasmussen and Williams\, Gaussian Processes for Machine Learning\, MIT Pr
 ess (2006)\n[6] Grisafi et al\, PRL\, 120\, 036002 (2018)
LOCATION:Mott Seminar (531) room\, top floor of the Mott Building\, in the
  Cavendish Laboratory\, West Cambridge.
END:VEVENT
END:VCALENDAR