SevenNet-Polar for MultiTask Prediction of Energy, Forces, Stress, and Born Effective Charges: Development and Application to ZrO$_2$, Li$_3$PO$_4$, and Perovskites

Abstract

Accurate prediction of the Born effective charge (BEC) tensor is crucial for modeling materials under electric fields but remains computationally expensive. To bridge this gap, we present SevenNet-Polar, an equivariant graph neural network framework based on the SevenNet architecture for fast and accurate BEC predictions. Our BEC-only predictors can achieve an RMSE as low as 0.0043 e on ZrO$_2$, Li$_3$PO$_4$, and perovskites, despite the presence of high-temperature (up to 2,000 K) and defect-laden training data. Our all-in-one multitask models for predicting energy, forces, stress, and BEC in ZrO$_2$ and Li$_3$PO$_4$ achieve high accuracy with an RMSE of 1.0 meV/atom for energy, 12 meV/angstrom for forces, 0.05 GPa for stress, and 0.0029 e for BEC. BEC accuracy is not degraded by multitask training. Scaling analysis reveals distinct exponents for diagonal and off-diagonal BEC components, both of which exhibit less favorable scaling than energy, force and stress errors. SevenNet-Polar generalizes robustly when tested on scenarios containing structural environments absent from the training set, such as along nudged elastic band (NEB) trajectories or grain boundaries in ZrO$_2$. Accelerated by FlashTP, SevenNet-Polar enables simulations containing up to 1.5 million atoms on multi-GPU supercomputers and up to approximately 15,000 atoms on a single consumer-grade GPU. This makes charge-aware molecular dynamics simulations under electric fields more accessible.