we used an in-house numerical optimization script to find optimal hyperparameters for the NN-model trained on ∆E of 1k SA-W data. However, as the outcome of NN training is not predictable and depends on the initial seeding of the network, the optimizer was continuously cycling. It could not converge even when we averaged the results over three independent data, and also on ∆E of 1k SA-AM data. While the larger (16k) and more complex (SA-AM) dataset preferred more complex NN models, in the overall ranking, the model presented in Table seems the most suitable for all the studied systems, and we will use this setup for the training of the following NNs. Although additional hyperparameter optimizations will likely not reach significantly more accurate models, they should be performed on new systems, as some of the 'optimal' hyperparameters are on the edge of the grid search values.