Fig. 6: Identification of an optimal effective charge range via training-based charge sweep.
a Mean absolute errors (MAEs) of energy, force, and stress as a function of effective charge (qeff). Results are shown for transfer learning (black), fine-tuning (blue), and evaluation on unseen tensile configurations (red). The shaded region (0.5–1.0 qe) highlights the range where accuracy is consistently improved across all metrics. b Parity plots comparing NNP predictions (with analytic Coulomb terms at q = ±0.75 qe) against DFT reference values for energy, forces, and stress. The model was trained only on MOMT-sampled configurations. Blue points correspond to MOMT data used in training; red crosses denote tensile configurations evaluated as unseen data. c Same as b, but for a model trained on the combined MOMT and tensile datasets. Inclusion of tensile configurations in training further improves model performance, confirming dataset extensibility and robustness across both equilibrium and non-equilibrium regimes.
