Rate Constants for Fine-Structure Excitations in O-H Collisions with
  Error Bars Obtained by Machine Learning
  We present an approach using a combination of coupled channel scattering
calculations with a machine- learning technique based on Gaussian Process
regression to determine the sensitivity of the rate constants for non-adiabatic
transitions in inelastic atomic collisions to variations of the underlying
adiabatic interaction potentials. Using this approach, we improve the previous
computations of the rate constants for the fine-structure transitions in
collisions of O(3Pj) with atomic H. We compute the error bars of the rate
constants corresponding to 20 % variations of the ab initio potentials and show
that this method can be used to determine which of the individual adiabatic
potentials are more or less important for the outcome of different
fine-structure changing collisions.
