To evaluate the quality of the generated MLP beyond figures of accuracy, we investigated a number of structural properties of the system used for training, i.e. the liquid, and of the crystal. Structural properties were evaluated over 100 ps (N, V, T ) trajectories at 300 K for the crystal and 1500 K for the liquid. The liquid underwent a preliminary 100 ps at 1500 K to ensure melting before collecting statistics. Here, we compare the properties obtained with what we consider as reference data, namely the AIMD simulations of the same systems produced in previous work. 34 In every case, we note an excellent reproduction of the ab initio properties. The MLP accurately emulates the crystal properties, thus demonstrating excellent transferability on this system, although it was trained on a different state (the liquid). Additionally, no change in coordination nor ring statistics is observed for the crystal (see Table ), and the computed porous volumes are fairly similar for every phase (see Table ). These data point to an excellent reproduction of the structural properties of both the system they were trained on,