Therefore, the larger the charge transfer, the bigger the dipole moment is. In this model, the dipole moment is a consequence of the charge transfer between the atoms within the molecule. 17 In particular, after studying hydrogen halide molecules, Pauling proposed that the dipole moment of a molecule is correlated with the relevance of the ionic structure with respect to the covalent one at the equilibrium bond length of the molecule.

The first explanation of the nature of the electric dipole moment of molecules is due to Pauling in the 1930s. 1 Molecules in the whole dataset classified by the types of their constituent atoms.Ģ An overview on the nature of the electric dipole moment of molecules The study of the nature of the electric dipole moment of molecules is a traditional topic in quantum chemistry that has fascinated the chemical physics community for almost a century by now. The molecular spectroscopic constants are needed in a combination that describes the force on the electrons at the equilibrium distance, i.e., in a combination that has the same functional dependence as the first derivative of the electronic kinetic energy at the equilibrium distance.įig. Our results reveal that it is not possible to predict the dipole moment of a molecule solely from atomic properties, although this is feasible for the spectroscopic constants, 9 but that it is necessary to include molecular features. The number of molecules in our dataset, classified by the type of the constituent atoms, is given in Fig. We show that, after compiling the most exhaustive list of dipole moments for diatomics up to date (to the best of our knowledge) into a dataset, it is possible to learn the dipole moment of diatomic molecules based upon atomic and molecular properties with a relative error ≲5%. In this paper, we present a data-driven approach to dipole moments of diatomic molecules and its relationship with spectroscopic constants. In particular, in chemical physics, as recently shown, data-driven approaches bring a new perspective to solve some of the most delicate problems of the field. When applied to physics, this novel paradigm lets nature speak up through hidden and intriguing correlations that lead to the formulation of new questions beyond a specific physical model. To be precise, adequately formatted data are used to identify unexpected correlations and to predict observables based on patterns and trends of the data. 12 In this new paradigm, machine learning techniques are among the most prominent tools to assess scientific knowledge. In the 2000s the big data-driven science paradigm emerged in the scientific community. 10,11 They also demonstrated that the dipole moment of some molecules can be predicted from the effective charge (obtained from quantum chemistry calculations) and spectroscopic constants of molecules. Where q is the effective charge and R e denotes the equilibrium bond length of the molecule, does not capture the proper physics of the dipole moment in many molecules. As a result, it has been found by Hou and Bernath that the expression for the dipole moment, d, taught in elementary chemistry courses Only recently, there have been some efforts towards the understanding of the dipole moment in terms of molecular spectroscopic constants. However, the electric dipole moment of a molecule, despite being an essential molecular property, has not been considered in previous studies about relationships between spectroscopic constants. 7,8 Recently, we have shown that some spectroscopic constants are universally related, 9 i.e., the relationships between them are independent of the nature of the molecular bond. 1 Introduction The study of relationships between spectroscopic constants is a traditional topic in chemical physics since the pioneering work of Kratzer and Mecke, among others 1–6 and is beautifully summarized by Varshini. 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. The dataset contains the dipole moment of 162 diatomic molecules, the most exhaustive and unbiased dataset of dipole moments up to date. 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%. 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.

Transform dipole moments into zmatrix