A hybrid molecular dynamics-machine learning framework for boiling point estimation in aromatic fluids

A Shateri and ZY Yang and N Sherkat and JF Xie, CASE STUDIES IN THERMAL ENGINEERING, 73, 106684 (2025).

DOI: 10.1016/j.csite.2025.106684

Precise estimation of boiling points in organic fluids is critical for designing efficient and safe thermal systems. This study presents a hybrid molecular dynamic (MD)-machine learning (ML) framework for boiling point estimation in two representative aromatic fluids: biphenyl (C12H10) and diphenyl ether (C12H10O). Two force fields, OPLS-AA and COMPASS, were tested in equilibrium MD simulations. OPLS-AA produced density predictions with a relative error below 2 % compared to experimental values, while COMPASS showed reduced accuracy at elevated temperatures. Boiling point was estimated using a density threshold method (yielding 525.66 K) and a thermodynamically rigorous inflection- point method (508.18 K), revealing similar to 3.3 % deviation between boiling onset and completion. MD data were used to train and evaluate three regression models-Nearest Neighbours Regression (NNR), Neural Network (NN), and Support Vector Regression (SVR). The NNR model achieved the best match with MD data, predicting a boiling point of 524.97 K and density of 0.064 g/cm(3). The NN model accurately estimated boiling temperature (525.3 K) but overestimated density, while SVR underestimated both. This work contributes a novel, interpretable MD-ML framework to integrate the inflection-point detection with data-driven model selection, offering a reproducible and accurate method for boiling point estimation that can be extended to other organic thermal systems.

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