Supplementary material for the article "Machine Learning Model to Predict Saturation Vapor Pressures of Atmospheric Aerosol Constituents"

Description

This is supplementary dataset for article "Machine Learning Model to Predict Saturation Vapor Pressures of Atmospheric Aerosol Constituents". It contains following elements: - Machine learning models (in Python) for predicting saturation vapor pressures using cosmo-files (COSMO-ML-psat.zip/cosmo-ml) - cosmo-files of the training data (COSMO-ML-psat.zip/cosmo-files) - cosmo-files of all conformers of the training compounds (cosmo-files-all-conformers.zip) All cosmo-files were calculated at the BP-TZVPD-FINE level of theory using TURBOMOLE version 7.7.

Year of publication

2024

Authors

University of Jyväskylä - Publisher

Kemian laitos

Hyttinen, Noora - Rights holder

Unknown

Hyttinen, Noora - Creator

Other information

Fields of science

Chemical sciences

Language

English

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Keywords

Atmospheric sciences, machine learning, COSMO, saturation vapor pressure, atmospheric scienes, höyrynpaine, ilmakehätieteet, koneoppiminen

Subject headings

ilmakehätieteet, koneoppiminen