This suggests average coordination number is a more complicated metric than average bond length and is more adept at capturing variances between structures. This leads to a broader training dataset which creates a more accurate model. Random sampling was unable to perform as well due to repeatedly sampling similar chemical environments (Figure ). This trend is supported in the learning curves of finetuned models of MACE-MP-0, SevenNet-0 and ORB-d3-v2 where coordination number sampling outperforms both bond length and random sampling (Supplementary Figures ). It is worth noting that the learning curves of ORB-d3-v2 for random and bond length sampling demonstrated more training frames did not produce a lower loss. The finetuning implementation in orbital does not use a test set likely leading to overfitting. However, finetuning ORB-d3-v2 using coordination number sampling showed improved learning. This suggests that coordination number sampling can explore structural diversity to the extent where the structures sampled continue to probe new environments and does not lead to overfitting. DFT data, there was significant gas formation as the structure broke apart at 2000 K. This structure is shown in Figure . This gaseous product had lower coordination environments which the MLP training data needed to properly capture to accurately describe pyrolysis mechanisms of CALF-20. The impact of this is seen in Figure where from 12 ps, there is a growing deviation between the DFT and MLP energy due to this product being poorly represented. This is most prominent in the random sampling but is also seen, to a lesser extent, in bond length and coordination number sampling methods.