# Rate Constants for Fine-Structure Excitations in O-H Collisions with Error Bars Obtained by Machine Learning

## TL;DR

This paper introduces a machine learning-enhanced method combining coupled channel calculations and Gaussian Process regression to accurately determine rate constants and their uncertainties for fine-structure transitions in O-H atomic collisions.

## Contribution

It presents a novel approach that integrates machine learning with quantum scattering calculations to assess sensitivity and error margins in collision rate constants.

## Key findings

- Improved computation of rate constants for O(3Pj) + H collisions.
- Quantified error bars for rate constants based on potential variations.
- Identified key adiabatic potentials influencing collision outcomes.

## Abstract

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.