Hybrid Approach for Predicting Melting Points in Nonionic Eutectic Solvents Using Thermodynamics and Machine Learning

Abstract

In this work, a hybrid approach combining solution thermodynamics and machine learning (ML) methods is presented as a means of estimating solid–liquid equilibria (SLE) in nonionic eutectic solvents. The models were developed based on a data set comprising 141 binary mixtures and 1668 experimental melting points. The semiempirical Associated Solution and Lattice (ASL) method was employed to characterize the SLE in two versions: with one fitting parameter, representing the interchange energy (ASL(ω)), and with two fitting parameters, representing the interchange energy and the heteroassociation constant (ASL(ω′,K)). This work compares models for predicting mixture melting points using direct ML and a hybrid approach. In the hybrid method, ML first predicts the ASL model’s fitting parameters, which are then used to calculate melting points. The single-parameter ASL approach showed better predictive performance than both the two-parameter ASL and direct ML predictions, achieving the lowest average absolute deviation of 8.7 K.