To improve model accuracy, a series of finetuned models were created from each foun- ). Sampling the phase space of these variables will create a diverse dataset which will capture extremities in behavior more effectively than random sampling (Figure ). This was achieved by combining the 300, 1000 and 2000 K datasets of each MOF and using a flat histogram sampling approach on the combined dataset. This led to more structures being sampled from the 2000 K dataset than the 300 K dataset as the higher temperatures showed greater variability in both average zinc coordination number and bond length (Supplementary Figures ). In addition, new MACE models were trained "from scratch" using the same features as MACE-MP-0 with these different sampling methods. All models were validated against a held-out dataset sampled randomly, taking 1000 structures from each equilibrated MOF-temperature DFT dataset. Coordination number and bond length sampling models learned better with fewer training frames and outperformed random sampling (Figure ). Moreover, the overall errors show coordination number sampling outperforms bond length sampling, shown in Table .