Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning

Knowledge of critical properties, such as critical temperature, pressure, density, as well as acentric factor, is essential to calculate thermo-physical properties of chemical compounds. Experiments to determine critical properties and acentric factors are expensive and time intensive; therefore, we...

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Bibliographic Details
Published in:Journal of chemical information and modeling 2023-08, Vol.63 (15), p.4574-4588
Main Authors: Biswas, Sayandeep, Chung, Yunsie, Ramirez, Josephine, Wu, Haoyang, Green, William H.
Format: Article
Language:English
Subjects:
Boiling points
Chemical compounds
Critical temperature
Enthalpy
Machine Learning
Machine Learning and Deep Learning
Melting points
Message passing
Molecular properties
Neural networks
Neural Networks, Computer
Physical properties
Public domain
Representations
Source code
Temperature
Training
Transition Temperature
Vaporization
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Summary:Knowledge of critical properties, such as critical temperature, pressure, density, as well as acentric factor, is essential to calculate thermo-physical properties of chemical compounds. Experiments to determine critical properties and acentric factors are expensive and time intensive; therefore, we developed a machine learning (ML) model that can predict these molecular properties given the SMILES representation of a chemical species. We explored directed message passing neural network (D-MPNN) and graph attention network as ML architecture choices. Additionally, we investigated featurization with additional atomic and molecular features, multitask training, and pretraining using estimated data to optimize model performance. Our final model utilizes a D-MPNN layer to learn the molecular representation and is supplemented by Abraham parameters. A multitask training scheme was used to train a single model to predict all the critical properties and acentric factors along with boiling point, melting point, enthalpy of vaporization, and enthalpy of fusion. The model was evaluated on both random and scaffold splits where it shows state-of-the-art accuracies. The extensive data set of critical properties and acentric factors contains 1144 chemical compounds and is made available in the public domain together with the source code that can be used for further exploration.
ISSN:1549-9596
1549-960X
DOI:10.1021/acs.jcim.3c00546