@inbook{c46ce0fca45a4a38ad042fe8378d4c09,
title = "Feature Embedding of Molecular Dynamics-Based Descriptors for Modeling Electrochemical Separation Processes",
abstract = "Machine learning is increasingly being used as a modeling technique for the analysis of complex systems even when limited knowledge is available. Here, we demonstrate the application of machine learning models to predict macroscopic properties of electrochemical polymers from molecular attributes. The computational framework proposes a novel approach to estimate molecular dynamic simulations (MD) attributes and experimental activity coefficients. Results showed that the data augmentation and embedding strategy effectively produce unique representations of each polymer. The findings from this study allow for the estimation of activity coefficients of novel polymers without the need for new time-consuming MD simulation runs. This data processing technique could then be used map to material properties that influence for electrochemical ionic separation units.",
author = "Dona, {H. K.Gallage} and T. Olayiwola and Briceno-Mena, {L. A.} and Arges, {C. G.} and R. Kumar and Romagnoli, {J. A.}",
note = "Publisher Copyright: {\textcopyright} 2023 Elsevier B.V.",
year = "2023",
month = jan,
doi = "10.1016/B978-0-443-15274-0.50231-6",
language = "English (US)",
series = "Computer Aided Chemical Engineering",
publisher = "Elsevier B.V.",
pages = "1451--1456",
booktitle = "Computer Aided Chemical Engineering",
}