Machine and deep learning models for predicting high pressure density of heterocyclic thiophenic compounds based on critical properties.

The multifaceted effects of the presence of thiophenic compounds on the environment are significant and cannot be overlooked. As heterocyclic compounds, thiophene and its derivatives play a significant role in materials science, particularly in the design of organic semiconductors, pharmaceuticals, and advanced polymers. Accurate prediction of their thermophysical properties is critical due to its impact on structural, thermal, and transport properties. This study utilizes state-of-the-art machine learning and deep learning models to predict high-pressure density of seven thiophene derivatives, namely thiophene, 2-methylthiophene, 3-methylthiophene, 2,5-dimethylthiophene, 2-thiophenemethanol, 2-thiophenecarboxaldehyde, and 2-acetylthiophene. The critical properties including critical temperature (T), critical pressure (P), critical volume (V), and acentric factor (ω), together with boiling point (T), and molecular weight (Mw) were used as input parameters. Models employed include Decision Tree (DT), Adaptive Boosting Decision Tree (AdaBoost-DT), Light Gradient Boosting Machine (LightGBM), Gradient Boosting (GBoost), TabNet, and Deep Neural Network (DNN). The statistical error evaluation showed that the LightGBM model showed superior performance with an average absolute percent relative error (AAPRE) of 0.0231, a root mean square error of 0.3499, and coefficient of determination (R) of 0.9999. The leverage method showed that 99.10 percent of the data was valid. These findings highlight the effectiveness of using critical properties as inputs and underscore the potential of the LightGBM model for reliable high-pressure density prediction of thiophene derivatives. This provides a robust tool for advancing materials science applications, and offers valuable insights for material design under extreme conditions.