Supplementary data for the article "Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions"
Hyttinen, Noora (2022). Supplementary data for the article "Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions". https://doi.org/10.17011/jyx/dataset/83604.
All authors: Hyttinen, Noora
Funders: Academy of Finland
Right-holders:
Availability and identifiers
Availability: Direct download
Publication year: 2022
URN identifier in JYX: http://urn.fi/URN:NBN:fi:jyu-202210194924
DOI identifier in JYX: https://doi.org/10.17011/jyx/dataset/83604
Description of the dataset
Description: The data set contains the supplementary data of the article "Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions" published in J. Phys. Chem. Lett., https://doi.org/10.1021/acs.jpclett.2c02612. The data includes:
- A machine learning (EMLM) model for predicting chemical potentials of individual conformers of multifunctional organic compounds calculated by the COSMOtherm program
- COSMO-files used for training and testing the EMLM model
- Descriptors and chemical potentials used for the training and testing the model
- A machine learning (EMLM) model for predicting chemical potentials of individual conformers of multifunctional organic compounds calculated by the COSMOtherm program
- COSMO-files used for training and testing the EMLM model
- Descriptors and chemical potentials used for the training and testing the model
Language: English
Free keywords: COSMO-RS; machine learning
Keywords (YSO): atmospheric sciences; machine learning
Fields of science: 116 Chemical sciences
Do you deal with data concerning special categories of personal data in your research?: No
Projects related to dataset
- Improving thermodynamic property estimates of SOA constituents using machine learning
- Hyttinen, Noora
- Academy of Finland
