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
Tekijät
Päivämäärä
2022Tekijänoikeudet
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.
Alkuperäislähde
https://nextcloud.jyu.fi/index.php/s/Sa2H5Teycxw9z9Q/downloadAineisto tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/ResearchDataset/155935998
Metadata
Näytä kaikki kuvailutiedotKokoelmat
- Tutkimusdata [284]
Rahoittaja(t)
Academy of Finland; Suomen AkatemiaRahoitusohjelmat(t)
Postdoctoral Researcher, AoF; Tutkijatohtori, SALisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
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Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions
Hyttinen, Noora; Pihlajamäki, Antti; Häkkinen, Hannu (American Chemical Society (ACS), 2022)We have trained the Extreme Minimum Learning Machine (EMLM) machine learning model to predict chemical potentials of individual conformers of multifunctional organic compounds containing carbon, hydrogen, and oxygen. The ... -
Supplementary data for the article "The effect of atmospherically relevant aminium salts on water uptake"
Hyttinen, Noora (University of Jyväskylä, 2023)Supplementary data for the article "The effect of atmospherically relevant aminium salts on water uptake". Contains a database of cosmo files used in the COSMOtherm calculations presented in the article. The files are ... -
Supplementary data for the article "Predicting liquid-liquid phase separation in ternary organic-organic-water mixtures"
Hyttinen, Noora (University of Jyväskylä, 2023)Artikkelin "Predicting liquid-liquid phase separation in ternary organic-organic-water mixtures" lisäaineisto. -
Machine Learning Model to Predict Saturation Vapor Pressures of Atmospheric Aerosol Constituents
Hyttinen, Noora; Li, Linjie; Hallquist, Mattias; Wu, Cheng (American Chemical Society, 2024)We present a novel machine learning (ML) model for predicting saturation vapor pressures (psat), a physical property of use to describe transport, distribution, mass transfer, and fate of environmental toxins and contaminants. ... -
Predicting hygroscopic growth of organosulfur aerosol particles using COSMOtherm
Li, Zijun; Buchholz, Angela; Hyttinen, Noora (Copernicus, 2024)Organosulfur (OS) compounds are important sulfur species in atmospheric aerosol particles, due to the reduction of global inorganic sulfur emissions. Understanding the physicochemical properties, such as hygroscopicity, ...
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