2023
DOI: 10.1021/acs.iecr.2c04559
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Interpretable Machine Learning Model for Predicting Interaction Energies between Dimethyl Sulfide and Potential Absorbing Solvents

Abstract: Non-bonding intermolecular interactions largely dominate the selective dissolution of trace species into physical solvents and, therefore, are fundamentally important to solvent development for the capture of environment-undesired compounds or purification of chemicals. However, acquirement of the interaction energy requires costly quantum chemical computation and still encounters a practical challenge to build a chemically interpretable machine learning (ML) prediction model using documented molecular descrip… Show more

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Cited by 4 publications
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