Explainable artificial intelligence models for estimating the heat capacity of deep eutectic solvents

Deep eutectic solvents (DES) are emerging as a promising alternative to traditional solvents due to their attractive characteristics, including low toxicity, biodegradability, ease of synthesis, and cost-effectiveness. Accurate knowledge of the physical properties of DES, such as heat capacity, is critical for their effective utilization in various applications. To complement expensive and time-consuming experimental measurements, this study presents a comprehensive investigation into the application of advanced machine learning techniques, including Convolutional Neural Networks (CNN), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM), for modelling the heat capacity of DES. The developed models were trained and validated using an extensive experimentally measured database comprising 2,696 datasets from 55 DES systems, covering a wide range of compositions and temperatures. The CNN model demonstrated superior performance compared to existing heat capacity correlations, achieving an Average Absolute Percentage Error (AAPE) of 0.982%, an R2 of 0.997, and a significantly reduced Root Mean Squared Error. The leverage approach was employed to ensure data reliability and confirm the robustness of the proposed paradigms. Moreover, the study utilized the Shapley Additive Explanations (SHAP) method to enhance the CNN model interpretability and validate the influence of input parameters. Physical validation through detailed trend analysis further confirmed the model's ability to preserve underlying physical relationships. In addition to its predictive accuracy, the proposed CNN model is designed for practical industrial applications. This work demonstrates how the model can be implemented to optimize DES selection and formulation in real-world scenarios, as illustrated by a case study presented in the paper. Overall, this study provides an efficient and reliable tool for the design and optimization of DES, enabling the rapid evaluation of suitable components and compositions while significantly reducing experimental effort.