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

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Ferraz Caetano, J (Author) FCUP; Teixeira, F (Author) Other; Cordeiro, MDS (Author) Other

Journal

Title: Chemosphere

Vol. 359

ISSN: 0045-6535

Publisher: Elsevier

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-010-E5Z

DOI: 10.1016/j.chemosphere.2024.142257

Abstract (EN): The accurate prediction of standard vaporization enthalpy (¿vapHm°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting ¿vapHm° 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 ¿vapHm° 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' ¿vapHm° 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 ¿vapHm° 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. © 2024 The Authors

Language: English

Type (Professor's evaluation): Scientific