  Machine Learning (ML) models have, in contrast to their usefulness in
molecular dynamics studies, had limited success as surrogate potentials for
reaction barrier search. It is due to the scarcity of training data in relevant
transition state regions of chemical space. Currently, available datasets for
training ML models on small molecular systems almost exclusively contain
configurations at or near equilibrium. In this work, we present the dataset
Transition1x containing 9.6 million Density Functional Theory (DFT)
calculations of forces and energies of molecular configurations on and around
reaction pathways at the wB97x/6-31G(d) level of theory. The data was generated
by running Nudged Elastic Band (NEB) calculations with DFT on 10k reactions
while saving intermediate calculations. We train state-of-the-art equivariant
graph message-passing neural network models on Transition1x and cross-validate
on the popular ANI1x and QM9 datasets. We show that ML models cannot learn
features in transition-state regions solely by training on hitherto popular
benchmark datasets. Transition1x is a new challenging benchmark that will
provide an important step towards developing next-generation ML force fields
that also work far away from equilibrium configurations and reactive systems.
