  The Kov\'ats Retention index (RI) is a quantity measured using gas
chromatography and commonly used in the identification of chemical structures.
Creating libraries of observed RI values is a laborious task, so we explore the
use of a deep neural network for predicting RI values from structure for
standard semipolar columns. This network generated predictions with a mean
absolute error of 15.1 and, in a quantification of the tail of the error
distribution, a 95th percentile absolute error of 46.5. Because of the
Artificial Intelligence Retention Indices (AIRI) network's accuracy, it was
used to predict RI values for the NIST EI-MS spectral libraries. These RI
values are used to improve chemical identification methods and the quality of
the library. Estimating uncertainty is an important practical need when using
prediction models. To quantify the uncertainty of our network for each
individual prediction, we used the outputs of an ensemble of 8 networks to
calculate a predicted standard deviation for each RI value prediction. This
predicted standard deviation was corrected to follow the error between observed
and predicted RI values. The Z scores using these predicted standard deviations
had a standard deviation of 1.52 and a 95th percentile absolute Z score
corresponding to a mean RI value of 42.6.
