论文标题
一种数据驱动的方法来确定双极分子的偶极矩
A data-driven approach to determine dipole moments of diatomic molecules
论文作者
论文摘要
我们提出了一种数据驱动的方法，用于预测双原子分子的电偶极矩，该分子是最相关的分子特性之一。特别是，我们将高斯过程回归应用于新的数据集，以表明可以学习双极分子的偶极矩，因此可以预测，相对误差<5％。该数据集包含162个硅藻分子的偶极矩，这是最新的偶极矩的最详尽和无偏的数据集。我们的发现表明，双原子分子的偶极力矩取决于成分原子的原子特性：电子亲和力和电离电位，以及在平衡距离处电子动能的第一个衍生物。
We present a data-driven approach for the prediction of the electric dipole moment of diatomic molecules, which is one of the most relevant molecular properties. In particular, we apply Gaussian process regression to a novel dataset to show that dipole moments of diatomic molecules can be learned, and hence predicted, with a relative error <5%. The dataset contains the dipole moment of 162 diatomic molecules, the most exhaustive and unbiased dataset of dipole moments up to date. Our findings show that the dipole moment of diatomic molecules depends on atomic properties of the constituents atoms: electron affinity and ionization potential, as well as on (a feature related to) the first derivative of the electronic kinetic energy at the equilibrium distance.
