A1 Journal article (refereed)
Machine Learning Model to Predict Saturation Vapor Pressures of Atmospheric Aerosol Constituents (2024)


Hyttinen, N., Li, L., Hallquist, M., & Wu, C. (2024). Machine Learning Model to Predict Saturation Vapor Pressures of Atmospheric Aerosol Constituents. ACS - ES & T Air, 1(9), 1156-1163. https://doi.org/10.1021/acsestair.4c00113


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Publication details

All authors or editorsHyttinen, Noora; Li, Linjie; Hallquist, Mattias; Wu, Cheng

Journal or seriesACS - ES & T Air

ISSN2837-1402

eISSN2837-1402

Publication year2024

Publication date22/07/2024

Volume1

Issue number9

Pages range1156-1163

PublisherAmerican Chemical Society

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1021/acsestair.4c00113

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/96632

Additional informationPublished as part of ACS ES&T Air virtual special issue “Elevating Atmospheric Chemistry Measurements and Modeling with Artificial Intelligence”


Abstract

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. The ML model uses σ-profiles from the conductor-like screening model (COSMO) as molecular descriptors. The main advantages in using σ-profiles instead of other types of molecular representations are the relatively small size of the descriptor and the fact that the addition of new elements does not affect the size of the descriptor. The ML model was trained separately for liquid and solid compounds using experimental vapor pressures at various temperatures. The 95% confidence intervals of the error in the liquid- and solid-phase log10(psat/Pa) are 1.02 and 1.4, respectively. Especially our solid-phase model outperforms all group-contribution models in predicting experimental sublimation pressures of solid compounds. To demonstrate its applicability, the model was used to predict psat of atmospherically relevant species, and the values were compared with those obtained from a new experimental method. Here, our model provided a tool for a better description of this critical property and gave a higher confidence in the measurements.


Keywordsatmosphere (earth)air impurities and contaminantsaerosolsmachine learningmodelling (representation)

Free keywordsCOSMO; extreme minimal learning machine; σ-profile; liquid; solid; volatility


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Ministry reportingYes

VIRTA submission year2024

Preliminary JUFO rating1


Last updated on 2025-16-01 at 20:06