Fig. 2: Limitations of direct learning of long-range interactions in NNP training. | npj Computational Materials

Fig. 2: Limitations of direct learning of long-range interactions in NNP training.

From: Neural network potentials with effective charge separation for non-equilibrium dynamics of ionic solids: a ZnO case study

Fig. 2: Limitations of direct learning of long-range interactions in NNP training.

a AL-based data distillation results, starting with an initial dataset of 2000 configurations selected from a pool of 20,000 generated via MOMT sampling using the SC-based order parameter. Plots show the mean absolute error (MAE) of the total 20,000 configurations for energy per atom (left), force (middle), and stress (right) as the number of training configurations increases. The model was trained using a loss function based solely on total energy (T-Train). b Parity plots comparing NNP predictions with reference values obtained from the total potential (Buck+Coul) for energy per atom (left), force (middle), and stress (right). Energy predictions exhibit excellent agreement (MAE < 5 meV/atom), while force and stress predictions show larger deviations, reflecting the challenges of learning derivative properties with short-range NNPs.

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