  Computational materials discovery has continually grown in utility over the
past decade due to advances in computing power and crystal structure prediction
algorithms (CSPA). However, the computational cost of the \textit{ab initio}
calculations required by CSPA limits its utility to small unit cells, reducing
the compositional and structural space the algorithms can explore. Past studies
have bypassed many unneeded \textit{ab initio} calculations by utilizing
machine learning methods to predict formation energy and determine the
stability of a material. Specifically, graph neural networks display high
fidelity in predicting formation energy. Traditionally graph neural networks
are trained on large data sets of relaxed structures. Unfortunately, the
geometries of unrelaxed candidate structures produced by CSPA often deviate
from the relaxed state, which leads to poor predictions hindering the model's
ability to filter energetically unfavorable prior to \textit{ab initio}
evaluation. This work shows that the prediction error on relaxed structures
reduces as training progresses, while the prediction error on unrelaxed
structures increases, suggesting an inverse correlation between relaxed and
unrelaxed structure prediction accuracy. To remedy this behavior, we propose a
simple, physically motivated, computationally cheap perturbation technique that
augments training data to improve predictions on unrelaxed structures
dramatically. On our test set consisting of 623 Nb-Sr-H hydride structures, we
found that training a crystal graph convolutional neural networks, utilizing
our augmentation method, reduced the MAE of formation energy prediction by 66\%
compared to training with only relaxed structures. We then show how this error
reduction can accelerates CSPA by improving the model's ability to filter out
energetically unfavorable structures accurately.
