AutoVap: An Interactive Machine Learning Tool for Predicting the Standard Enthalpy of Vaporization

ABSTRACT This communication introduces AutoVap ( https://autovap.jfcaetano.com ), an interactive web application for predicting the standard molar enthalpy of vaporization (Δ vap H m °) of chemical structures. Built on an implementation of a supervised machine learning model with RDKit‐derived descriptors, AutoVap achieves predictions with a R 2 test of 96%, across diverse chemical families. The platform allows users to input SMILES notation and obtain both predictions and associated uncertainties through a simple interface. Its validated accuracy and interpretability highlight AutoVap as a reliable and accessible tool, with the potential to advance applications in thermodynamic property estimation, reaction yield prediction, and molecular design.