ΔvapHm-RDKit: Standard Vaporization Enthalpy Database

We present a database to Predict Standard Vaporization Enthalpies through Intelligible Features.
This database presents a variety of molecules of diverse chemical families with known experimental Standard Vaporization Enthalpy (ΔvapHm°) values. Entries were collected from the NIST WebBook Library through a web scraping routine, considering only ΔvapHm° experimental values, ranging from 15 kJ mol-1 to 400 kJ mol-1. Experimental value scrapping was automatically made, without duplicates, by selecting the first listed value (either a from single or average of values as determined by NIST). The database includes each value's original literature, along with experimental uncertainties (when available). 
Each compound is represented by its molecular family, SMILES string and respective InChlKey & InChl identifiers. The curated database consists of 1781 molecules over 15 different chemical families. The chemical descriptors used to feed our model were generated using RDKit version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the "MolFromSmiles" function in "RDKIT.Chem" as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and ΔvapHm° determinations.
Through Machine Learning regression algorithms, we have developed models that were able to make accurate ΔvapHm° predictions, based on the information encoded in each chemical feature. Our models can be accessed through GitHub.