NNP training and simulations used the Python package SchNet-Pack together with the atomistic simulation environment (ASE) . We trained the ensemble of six NNPs using the same setup of the SchNet architecture as in our previous work starting from the initial silica models : a cutoff of 6 Å, six interaction blocks, 128 feature vector elements, and distance expansion with 60 Gaussians. The generated DFT database was randomly split into training (80%), validation (10%), and test sets (10%). We employed the ADAM optimizer with 8 structures per batch for minimization of the mean squared error of energy and forces (with a trade-off factor of 0.01, see also Ref ). The learning rate was halved if the value of the validation loss did not decrease in 15 epochs (from 10 -4 to 10 -6 ).