The MLP models in this study were constructed using NequIP version 0.5.6 and Allegro (which does not have released version numbers). The models employed a cutoff radius chosen between 4 Å and 6 Å for the atomic environments and used five interaction layers. Feature representations were restricted to maximal rotation orders of either l = 1 or l = 2, and for the largest cutoff radius (6 Å) features with odd mirror parity were neglected. The loss function consists of a weighted average of potential energy and force errors, and was optimized using the Adam algorithm and a learning rate of 0.005. We tested the choice of hyperparameters on the 1000-100 train-test data set with random sampling to determine the influence of rotation orders and cutoff radius. We also compared the choice of random or flat-histogram. This lead us to choose a production model that used l = 2 and a cut-off radius of 6 Å without parity as this enabled the highest accuracy with good efficiency. The production model was trained on the final flat-histogram data-set featuring 2357-190 samples.