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arXiv 2210.09784physics.chem-phcs.LG
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化学精确密度泛函理论的无规相近似广义多体色散修正

Generalized Many-Body Dispersion Correction through Random-phase Approximation for Chemically Accurate Density Functional Theory

CNRS(法国国家科学研究中心)

Pier Paolo Poier, Louis Lagardère, Jean-Philip Piquemal

摘要:

我们使用广义随机相位近似(RPA)形式主义将我们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率(Q)项,从而能够包含范德华贡献。所得到的DNN-MBDQ模型只依赖于从头算-导出量,因为引入的四极极化率是从偶极极化率递归检索的,进而通过Tkartchenko-Scheffler方法建模。一个可转移的和有效的深层神经网络(DNN)提供原子在分子体积,而一个单一的距离分离参数被用来耦合模型的密度泛函理论(DFT)。由于它可以以可忽略的成本计算,DNN-MBDQ方法可以与DFT泛函(如PBE,PBE 0和B86 bPBE(无色散))相结合。DNN-MBQ校正的泛函达到化学精度,同时与仅偶极对应物相比显示出较低的误差。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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