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SUMMARY:Validation of Prediction Uncertainty in Computational Chemistry - 
 Pascal Pernot\, CNRS\, Université Paris-Sud
DTSTART:20220221T140000Z
DTEND:20220221T143000Z
UID:TALK167285@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:Uncertainty quantification (UQ) in computational chemistry (CC
 ) is still in its infancy. Very few CC methods are designed to provide a c
 onfidence level on their predictions\, and most users still rely improperl
 y on the mean absolute error as an accuracy metric. The development of rel
 iable uncertainty quantification methods is essential\, notably for comput
 ational chemistry to be used confidently in industrial processes.\n\nA rev
 iew of the CC-UQ literature shows that there is no common standard procedu
 re to report nor validate prediction uncertainty. I consider here analysis
  tools using concepts (calibration and sharpness) developed in meteorology
  and machine learning for the validation of probabilistic forecasters. The
 se tools are adapted to CC-UQ and applied to datasets of prediction uncert
 ainties provided by composite methods\, Bayesian Ensembles methods\, machi
 ne learning and a -posteriori statistical methods. [P. Pernot (2022) https
 ://arxiv.org/abs/2201.01511] 
LOCATION:Venue to be confirmed
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