用于能量、力、应力和玻恩有效电荷多任务预测的SevenNet-Polar：在ZrO$_2$、Li$_3$PO$_4$和钙钛矿中的开发与应用
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
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中文总结 AI 辅助
研究针对电场下材料建模中玻恩有效电荷张量预测计算昂贵的问题，提出SevenNet-Polar等变图神经网络框架，其在多任务预测上精度高，缩放分析有发现，新场景测试表现好且能加速模拟，提升电荷感知分子动力学模拟的可及性。
中文摘要 AI 辅助
准确预测玻恩有效电荷（BEC）张量对于电场下材料建模至关重要，但计算成本高昂。为此提出SevenNet-Polar，这是基于SevenNet架构的等变图神经网络框架，用于快速准确的BEC预测。仅BEC预测器在ZrO$_2$、Li$_3$PO$_4$和钙钛矿上RMSE低至0.0043 e。多任务模型在ZrO$_2$和Li$_3$PO$_4$中预测能量、力、应力和BEC也有高精度。缩放分析揭示BEC不同分量指数。SevenNet-Polar在新场景测试中表现良好，且能加速模拟，使电场下电荷感知分子动力学模拟更易实现。
英文摘要
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.
