Data-driven, explainable machine learning model for predicting volatile organic compounds' standard vaporization enthalpy

Abstract

The accurate prediction of standard vaporization enthalpy (Δ_vapH_m°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting Δ_vapH_m° of VOCs, machine learning (ML) models enable a high-throughput, cost-effective property estimation. But despite a rising momentum, existing ML algorithms still present limitations in prediction accuracy and broad chemical applications. In this work, we present a data driven, explainable supervised ML model to predict Δ_vapH_m° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs' Δ_vapH_m° with a mean absolute error of 3.02 kJ mol^-1 and a 95% test score. The model was successfully validated through the prediction of Δ_vapH_m° for a known database of VOCs and through molecular group hold-out tests. Through chemical feature importance analysis, this explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model's prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs.

Keywords: Machine learning; Standard vaporization enthalpy; Supervised learning; Thermochemical predictions; VOC.