A1 Journal article (refereed)
Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions (2022)

Hyttinen, N., Pihlajamäki, A., & Häkkinen, H. (2022). Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions. Journal of Physical Chemistry Letters, 13(42), 9928-9933. https://doi.org/10.1021/acs.jpclett.2c02612

JYU authors or editors

Hyttinen, Noora
Pihlajamäki, Antti
Häkkinen, Hannu

Publication details

All authors or editors: Hyttinen, Noora; Pihlajamäki, Antti; Häkkinen, Hannu

Journal or series: Journal of Physical Chemistry Letters

eISSN: 1948-7185

Publication year: 2022

Publication date: 19/10/2022

Volume: 13

Issue number: 42

Pages range: 9928-9933

Publisher: American Chemical Society (ACS)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1021/acs.jpclett.2c02612

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/83856

Abstract

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 model is able to predict chemical potentials of molecules that are in the size range of the training data with a root-mean-square error (RMSE) of 0.5 kcal/mol. There is also a linear correlation between calculated and predicted chemical potentials of molecules that are larger than those included in the training set. Finding the lowest chemical potential conformers is useful in condensed phase thermodynamic property calculations, in order to reduce the number of computationally demanding density functional theory calculations.

Keywords: organic compounds; potential energy; thermodynamics; thermochemistry; atmospheric chemistry; computational chemistry; density functional theory; machine learning