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• W4385189160 endingPage "4588" @default.
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• W4385189160 abstract "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." @default.
• W4385189160 created "2023-07-25" @default.
• W4385189160 creator A5006469120 @default.
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• W4385189160 creator A5062459841 @default.
• W4385189160 date "2023-07-24" @default.
• W4385189160 modified "2023-10-16" @default.
• W4385189160 title "Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning" @default.
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• W4385189160 doi "https://doi.org/10.1021/acs.jcim.3c00546" @default.

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