  Quantum mechanical methods like Density Functional Theory (DFT) are used with
great success alongside efficient search algorithms for studying kinetics of
reactive systems. However, DFT is prohibitively expensive for large scale
exploration. Machine Learning (ML) models have turned out to be excellent
emulators of small molecule DFT calculations and could possibly replace DFT in
such tasks. For kinetics, success relies primarily on the models capability to
accurately predict the Potential Energy Surface (PES) around transition-states
and Minimal Energy Paths (MEPs). Previously this has not been possible due to
scarcity of relevant data in the literature. In this paper we train state of
the art equivariant Graph Neural Network (GNN)-based models on around 10.000
elementary reactions from the Transition1x dataset. We apply the models as
potentials for the Nudged Elastic Band (NEB) algorithm and achieve a Mean
Average Error (MAE) of 0.13+/-0.03 eV on barrier energies on unseen reactions.
We compare the results against equivalent models trained on QM9 and ANI1x. We
also compare with and outperform Density Functional based Tight Binding (DFTB)
on both accuracy and computational resource. The implication is that ML models,
given relevant data, are now at a level where they can be applied for
downstream tasks in quantum chemistry transcending prediction of simple
molecular features.
