结合原子部分电荷和原子偶极子预测分子的偶极矩
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
摘要：
分子偶极矩（$\boldsymbolμ$）是化学中的一个中心量。它是必不可少的预测红外和和频产生光谱，以及感应和远程静电相互作用。此外，它可以直接从高级量子力学计算中提取，使其成为机器学习（ML）的理想目标。在这项工作中，我们选择用一个物理启发的ML模型来表示这个量，该模型捕获了两种不同的物理效应：在自适应高斯过程回归（SA-GPR）框架内捕获局部原子极化，该框架为每个原子分配（矢量）偶极矩，而通过为每个原子分配部分（标量）电荷来捕获整个分子中的电荷运动。将得到的“MuML”模型拟合在一起，以重现使用高级耦合簇理论（CCSD）和密度泛函理论（DFT）在QM 7 b数据集上计算的分子$\boldsymbolμ$。当应用于更大更复杂的分子的展示数据集时，组合模型显示出优异的可移植性，以计算成本的一小部分接近DFT的精度。我们还表明，在预测的不确定性可以可靠地估计使用校准委员会模型。然而，模型的最终性能取决于手头系统的细节，当描述其偶极子几乎完全由电荷分离产生的大分子时，标量模型显然更优越。这些观察点的重要性，同时占本地和非本地的影响，有助于$\boldsymbolμ$ ;此外，他们定义了一个具有挑战性的任务，基准未来的模型，特别是那些旨在描述凝聚相。
英文摘要：
The molecular dipole moment ($\boldsymbolμ$) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra, as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). In this work, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression (SA-GPR) framework, which assigns a (vector) dipole moment to each atom, while movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting "MuML" models are fitted together to reproduce molecular $\boldsymbolμ$ computed using high-level coupled-cluster theory (CCSD) and density functional theory (DFT) on the QM7b dataset. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to $\boldsymbolμ$; further, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.
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