Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions

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.

Automated summary

In this study, we trained an Extreme Minimum Learning Machine (EMLM) machine learning model to predict the chemical potentials of individual conformers of multifunctional organic compounds containing carbon, hydrogen, and oxygen. The model accurately predicts the chemical potentials within the size range of the training data, and also shows a certain level of predictive performance for molecules that are larger than the training set.