Exploring molecular interactions and dielectric relaxation in n-octanol/DMF binary mixtures: a machine...

Area of Science:

• Physical Chemistry
• Materials Science
• Computational Chemistry

Background:

• Understanding the dielectric properties of liquid mixtures is crucial for various applications.
• Complex permittivity spectra (CPS) provide insights into molecular interactions and structural dynamics.
• Traditional analysis methods can be time-consuming and labor-intensive.

Purpose of the Study:

• To investigate the complex permittivity spectra of n-Octanol and N,N-Dimethylformamide (DMF) binary mixtures.
• To analyze molecular interactions and structural characteristics using dielectric relaxation models.
• To apply and evaluate machine learning models for predicting dielectric properties.

Main Methods:

• Measurements of complex permittivity spectra (CPS) using a vector network analyzer (VNA).
• Fitting data to dielectric relaxation models (e.g., Cole-Cole) to determine parameters like static dielectric constant and relaxation time.
• Application of machine learning algorithms (LightGBM, MLP, Gradient Boosting) for predictive modeling.

Main Results:

• Dielectric parameters (static dielectric constant, relaxation strength, relaxation time) were determined and analyzed.
• Excess dielectric parameters and correlation factors revealed molecular interactions.
• LightGBM demonstrated superior accuracy in predicting dielectric properties (ε' and ε″) compared to other models.

Conclusions:

• The study successfully characterized the dielectric behavior of n-Octanol/DMF mixtures.
• Machine learning, especially LightGBM, offers an efficient and accurate method for predicting dielectric properties.
• Integrating experimental data with machine learning accelerates the study of complex liquid systems.