论文标题
精确的深度学习辅助密度策略，用于多体色散校正的密度理论
Accurate Deep Learning-aided Density-free Strategy for Many-Body Dispersion-corrected Density Functional Theory
论文作者
论文摘要
使用对大型有机分子的大型ANI-1数据集训练的深神经网络模型（DNN），我们提出了一种无传输密度的多体色散模型（DNN-MBD）。 DNN策略绕过了MBD模型所需的Kohn-Sham电子密度的显式赫希菲尔德分区，以获得Tkatchenko-Scheffler极化续订的原子量。所得的DNN-MBD模型以最小的基础迭代股东原子量训练，并与密度功能理论（DFT）结合使用，与基于不同分区方案的其他方法相比（如果不是更大）的精度（如果不是更大）。 DNN-MBD模型在Tinker-HP软件包中实现，与执行显式密度分配的MBD模型相比，总体计算成本降低了整体计算成本。它与最近引入的MBD方程的随机公式（J.Chemy。Comput。，2022，18，3，1633-1645）耦合，可在保留的准确性下实现大型常规分散式DFT计算。此外，DNN电子密度的特征将MBD的适用性扩展到了诸如力场和神经网络之类的方法论之外。
Using a Deep Neuronal Network model (DNN) trained on the large ANI-1 data set of small organic molecules, we propose a transferable density-free many-body dispersion model (DNN-MBD). The DNN strategy bypasses the explicit Hirshfeld partitioning of the Kohn-Sham electron density required by MBD models to obtain the atom-in-molecules volumes used by the Tkatchenko-Scheffler polarizability rescaling. The resulting DNN-MBD model is trained with minimal basis iterative Stockholder atomic volumes and, coupled to Density Functional Theory (DFT), exhibits comparable (if not greater) accuracy to other approaches based on different partitioning schemes. Implemented in the Tinker-HP package, the DNN-MBD model decreases the overall computational cost compared to MBD models where the explicit density partitioning is performed. Its coupling with the recently introduced Stochastic formulation of the MBD equations (J. Chem. Theory. Comput., 2022, 18, 3, 1633-1645) enables large routine dispersion-corrected DFT calculations at preserved accuracy. Furthermore, the DNN electron density-free features extend MBD's applicability beyond electronic structure theory within methodologies such as force fields and neural networks.
