在矩张量势与等变张量网络势中纳入固定电荷的库仑相互作用
Incorporating Coulomb interactions with fixed charges in Moment Tensor Potentials and Equivariant Tensor Network Potentials
AI总结：
本文将固定电荷库仑长程静电相互作用显式纳入矩张量势和等变张量网络势，使带电分子有机二聚体的能量拟合误差降低四倍以上，并显著改善结合曲线预测，结果与密度泛函理论一致。
AI中文摘要：
在本工作中，我们将以固定电荷库仑模型形式的长程静电相互作用纳入短程机器学习原子间势（MLIPs）的泛函形式中，特别是矩张量势（Moment Tensor Potential）和等变张量网络势（Equivariant Tensor Network potential）。我们表明，显式纳入固定电荷的库仑相互作用可显著降低在带电分子有机二聚体上训练的短程 MLIPs 的能量拟合误差，即降低四倍以上。此外，利用我们的长程模型，我们证明了在预测带电分子有机二聚体的结合曲线方面有显著改进。最后，我们表明，对于带电分子有机二聚体，用 MLIPs 计算得到的结果与用密度泛函理论得到的结果具有良好的一致性。
英文摘要：
In this work, we incorporate long-range electrostatic interactions in the form of the Coulomb model with fixed charges into the functional form of short-range machine-learning interatomic potentials (MLIPs), particularly in the Moment Tensor Potential and Equivariant Tensor Network potential. We show that explicit incorporation of the Coulomb interactions with fixed charges leads to a significant reduction of energy fitting errors, namely, more than four times, of short-range MLIPs trained on organic dimers of charged molecules. Furthermore, with our long-range models we demonstrate a significant improvement in the prediction of the binding curves of the organic dimers of charged molecules. Finally, we show that the results calculated with MLIPs are in good correspondence with those obtained with density functional theory for organic dimers of charged molecules.
