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Validation of Prediction Uncertainty in Computational Chemistry

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  • UserPascal Pernot, CNRS, Universit√© Paris-Sud World_link
  • ClockMonday 21 February 2022, 14:00-14:30
  • HouseVenue to be confirmed.

If you have a question about this talk, please contact Dr Christoph Schran.

Zoom details: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT09

Uncertainty quantification (UQ) in computational chemistry (CC) is still in its infancy. Very few CC methods are designed to provide a confidence level on their predictions, and most users still rely improperly on the mean absolute error as an accuracy metric. The development of reliable uncertainty quantification methods is essential, notably for computational chemistry to be used confidently in industrial processes.

A review of the CC-UQ literature shows that there is no common standard procedure 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. These tools are adapted to CC-UQ and applied to datasets of prediction uncertainties provided by composite methods, Bayesian Ensembles methods, machine learning and a -posteriori statistical methods. [P. Pernot (2022) https://arxiv.org/abs/2201.01511]

This talk is part of the Lennard-Jones Centre series.

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