Fig. 5: Performance of transfer-learned NNPs with different charge choices. | npj Computational Materials

Fig. 5: Performance of transfer-learned NNPs with different charge choices.

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

Fig. 5: Performance of transfer-learned NNPs with different charge choices.

Parity plots comparing model predictions and DFT reference values for energy per atom (left), atomic forces (middle), and stress (right). a Performance of the first NNP model, pretrained and fine-tuned directly on total DFT quantities without explicit Coulomb interactions. b Predictions from a separate NNP model trained on DFT residuals (i.e., DFT - Coulombic terms), with Coulomb interactions analytically added back during evaluation to account for effective atomic charges (q=±1.2 qe). c Evaluation of the same residual-trained NNP model directly against DFT residual targets.

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