Zirconium Machine Learned Potential Trained on a Euclidean Neural Network

To curb the computational costs from density functional theory molecular dynamics (DFT-MD) simulations, we explore the use of a machine learned potential. With the use of a Euclidean tensor field neural network (E3NN), we train a dataset of body-centered cubic (bcc) Zirconium (Zr). Our dataset consists of stochastic snapshots with 216 atoms, 1000 steps with 2 fs time steps, and a temperature range of 1200-1640K. We train a model to predict forces to then compute the systems' thermodynamic properties. For the best results in minimizing train and test set prediction errors, we use a novel active learning algorithm and converge the loss function to a set value. Initially, we start training a model with 100 steps and a loss convergence of 0.0050 resulting in a relatively high test set error of 0.7 eV/Å. We then take the highest 10% of test set errors and re-sort them into the train set. This is repeated until the model is fitted appropriately. Trained on 613 steps, the resulting model has a median test set error of 0.06 eV/Å, a magnitude lower than our initial error.