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arXiv 2007.13228physics.chem-ph
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数据驱动的双原子分子偶极矩测定方法

A data-driven approach to determine dipole moments of diatomic molecules

Xiangyue Liu, Gerard Meijer, Jesús Pérez-Ríos

摘要:

我们提出了一个数据驱动的方法来预测双原子分子的电偶极矩,这是最相关的分子性质之一。特别地,我们将高斯过程回归应用于一个新的数据集,以表明双原子分子的偶极矩可以被学习,从而预测,相对误差<5%。该数据集包含162个双原子分子的偶极矩,这是迄今为止最详尽和无偏的偶极矩数据集。我们的研究结果表明,双原子分子的偶极矩取决于组成原子的原子性质:电子亲合势和电离势,以及(一个功能相关)的一阶导数的电子动能在平衡距离。

英文摘要:

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.

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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