Calculation of the Kirkwood–Frohlich correlation factor and dielectric constant of methanol using a statistical model and density functional theory

Development of an innovative data-driven system to generate descriptive prediction equation of dielectric constant on small sample sets

Dielectric constant (DC, ε) is a fundamental parameter in material sciences to measure polarizability of the system. In industrial processes, its value is an imperative indicator, which demonstrates the dielectric property of material and compiles information including separation information, chemical equilibrium, chemical reactivity analysis, and solubility modeling. Since, the available ε-prediction models are fairly primitive and frequently suffer from serious failures especially when deals with strong polar compounds. Therefore, we have developed a novel data-driven system to improve the efficiency and wide-range applicability of ε using in material sciences. This innovative scheme adopts the correlation distance and genetic algorithm to discriminate features’ combination and avoid overfitting. Herein, the prediction output of the single ML model as a coding to estimate the target value by simulating the layer-by-layer extraction in deep learning, and enabling instant search for the optimal combination of features is recruited. Our model established an improved correlation value of 0.956 with target as compared to the previously available best traditional ML result of 0.877. Our framework established a profound improvement, especially for material systems possessing ε value >50. In terms of interpretability, we have derived a conceptual computational equation from a minimum generating tree. Our innovative data-driven system is preferentially superior over other methods due to its application for the prediction of dielectric constants as well as for the prediction of overall micro and macro-properties of any multi-components complex.

Predicting dielectric constants of pure liquids: fragment-based Kirkwood-Fröhlich model applicable over a wide range of polarity

In view of developing a procedure to predict the dielectric constant (ε_r) of pure liquids from molecular structure, a thorough analysis of prominent factors affecting this property is carried out. The results suggest that the orientational dipolar parameter gμ² involved in the Kirkwood-Fröhlich theory may be estimated as a sum of additive contributions (gμ²)_i associated with suitably defined polar fragments i. Associated with third-party models for the molar volume V_m and the refractive index n_D, this provides a practical route to predicting ε_r for new compounds. Advantages over previous methods include: simplicity, as the present model relies on fragment-additivity and does not require 3D structures; sound physical bases; demonstrated applicability to polar liquids with ε_r values up to 200; predictive ability extensively demonstrated against large datasets (for a total of 1220 compounds) covering a broad structural diversity, resulting in values of the root mean square deviation/average percent error as low as 3.7/10% for data sets focused on simple organic compounds as considered in previous studies, although the inclusion of many alcohols in the data set leads to poorer statistics (5.0/32%) due to the lack of specific parameters for hydroxyl groups in distinct environments. The approach should be of special interest in the current search for new aprotic electrolytes aimed at improving the performances of electrochemical energy storage systems. Although its reliance on many fitting parameters restricts its domain of applicability, the present implementation is recommended over current procedures whenever possible. A Python script is provided to allow its straightforward application.