  We present evidence that learned density functional theory (``DFT'') force
fields are ready for ground state catalyst discovery. Our key finding is that
relaxation using forces from a learned potential yields structures with similar
or lower energy to those relaxed using the RPBE functional in over 50\% of
evaluated systems, despite the fact that the predicted forces differ
significantly from the ground truth. This has the surprising implication that
learned potentials may be ready for replacing DFT in challenging catalytic
systems such as those found in the Open Catalyst 2020 dataset. Furthermore, we
show that a force field trained on a locally harmonic energy surface with the
same minima as a target DFT energy is also able to find lower or similar energy
structures in over 50\% of cases. This ``Easy Potential'' converges in fewer
steps than a standard model trained on true energies and forces, which further
accelerates calculations. Its success illustrates a key point: learned
potentials can locate energy minima even when the model has high force errors.
The main requirement for structure optimisation is simply that the learned
potential has the correct minima. Since learned potentials are fast and scale
linearly with system size, our results open the possibility of quickly finding
ground states for large systems.
