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SUMMARY:Reliable and Efficient Regioselectivity Predictions Using an Integr
 ated DFT-ML Approach
LOCATION:Chemistry A101
TZID:America/Denver
DTSTART:20231026T160000
UID:2026-03-16-00-01-27@natsci.colostate.edu
DTSTAMP:20260316T000127
Description:Abstract:\n\nThe application of machine learning to chemistry h
 as seen tremendous success in recent times. However\, in most cases the ap
 plicability of the trained models from such studies is limited to points s
 imilar to training data\, with far-off and borderline data points being ex
 tremely prone to errors. To handle such error-prone data points alternativ
 e approaches must be adopted. In an attempt to automate this scenario Guan
  and co-workers propose an automated workflow [1] which triggers expensive
  alternative computations\, when the model is uncertain about its predicti
 ons. The work not only achieves impressive accuracy on benchmarks but also
  lays down the foundation for future work in uncertainty aware prediction.
  In this seminar\, I will discuss the foundational aspects of uncertainty 
 quantification implemented by the workflow\, and how they can be used to o
 btain uncertainty aware predictions from a machine learning model.\n\n1. G
 uan Y.\; Lee T.\; Wang K.\; Yu S.\; McWilliams J.\; J. Chem. Inf. Model. 2
 023\, 63\, 12\, 3751-3760 4:00 pm
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