Supplementary data for the article "Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions"
Hyttinen, Noora. Supplementary data for the article "Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions". 10.17011/jyx/dataset/83604
Authors
Date
2022Copyright
Hyttinen, Noora and University of Jyväskylä
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 Artikkelin "Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions" lisäaineisto.
Original source
https://nextcloud.jyu.fi/index.php/s/Sa2H5Teycxw9z9Q/downloadDataset in research information system
https://converis.jyu.fi/converis/portal/detail/ResearchDataset/155935998
Metadata
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- Tutkimusdata [284]
Related funder(s)
Academy of Finland; Suomen AkatemiaFunding program(s)
Postdoctoral Researcher, AoF; Tutkijatohtori, SALicense
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International
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