The filter (embedding) network has three layers with (25, 50, 100) nodes/layer, and the fitting net is composed of three layers, with 240 nodes each. The network is trained with the ADAM optimizer, with an exponentially decaying learning rate from 1.0 × 10 - 3 to 5.0× 10 -8 . During the optimization process, the pre-factors in the loss function change from 1 to 10 and 1000 to 1 for the energy and force terms, respectively. The final NN model used for the production run was trained for 1.0 × 10 6 steps. Additional configurations were obtained using an active learning sampling procedure as implemented in the DP-GEN package . Four NN potentials were trained on the same dataset with different initializations of weights and biases. NVT-MD simulations were performed at multiple temperatures (300, 3000, and 4000 K) using the LAMMPS package . By comparing the structures from MD simulations, the agreement on the force predictions made by these potentials is used to select new configurations. When the deviation of NN prediction for one configuration was in the range of [0.4, 0.8] eV/Å, the corresponding structure was labeled as a candidate for the training set. The upper limit is used to filter nonphysical configurations .