论文标题
通过随机相位近似校正广义的多体色散校正，以实现化学精确的密度理论
Generalized Many-Body Dispersion Correction through Random-phase Approximation for Chemically Accurate Density Functional Theory
论文作者
论文摘要
我们使用广义的随机相位近似（RPA）形式主义将最近提出的深度学习辅助多体色散（DNN-MBD）模型扩展到四极极化性（Q）项，从而使范德华的贡献包括在偶极子之外。所得的DNN-MBDQ模型仅依赖于从头启用的数量，因为引入的四极极化是从偶极子递回检索的，进而通过Tkatchenko-Scheffler方法建模。可转移且有效的深神经网络（DNN）提供了分子体积的原子，而单个范围分离参数则用于将模型搭配到密度功能理论（DFT）。由于可以以微不足道的成本计算，因此DNN-MBDQ方法可以与DFT功能（例如PBE，PBE0和B86BPBE（无分散））结合使用。与仅偶极子的功能相比，通过DNN-MBQ校正的功能达到化学精度，同时表现出较低的误差。
We extend our recently proposed Deep Learning-aided many-body dispersion (DNN-MBD) model to quadrupole polarizability (Q) terms using a generalized Random Phase Approximation (RPA) formalism, thus enabling the inclusion of van der Waals contributions beyond dipole. The resulting DNN-MBDQ model only relies on ab initio-derived quantities as the introduced quadrupole polarizabilities are recursively retrieved from dipole ones, in turn modelled via the Tkatchenko-Scheffler method. A transferable and efficient deep-neuronal network (DNN) provides atom in molecule volumes, while a single range-separation parameter is used to couple the model to Density Functional Theory (DFT). Since it can be computed at a negligible cost, the DNN-MBDQ approach can be coupled with DFT functionals such as PBE,PBE0 and B86bPBE (dispersionless). The DNN-MBQ-corrected functionals reach chemical accuracy while exhibiting lower errors compared to their dipole-only counterparts.
