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\begin{document}

\title{Neural Recommender System for Activity Coefficient Prediction and UNIFAC
Model Extension of Ionic Liquid-Solute Systems}



\author[1]{Guzhong Chen}%
\author[2]{Zhen Song}%
\author[1]{Zhiwen Qi}%
\author[3]{Kai Sundmacher}%
\affil[1]{East China University of Science and Technology}%
\affil[2]{Max-Planck-Institut fur Dynamik komplexer technischer Systeme}%
\affil[3]{Max Planck Institute for Dynamics of Complex Technical Systems}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\begin{abstract}
For the ionic liquid (IL)-solute systems of broad interest, a deep
neural network based recommender system (RS) for predicting the infinite
dilution activity coefficient (\selectlanguage{greek}γ\selectlanguage{english}[?]\selectlanguage{english}) is proposed and applied for a large
extension of the UNIFAC model. In the RS, neural network entity
embeddings are employed for mapping each IL and solute and neural
collaborative filtering is utilized to handle the nonlinearities of
IL-solute interactions. A comprehensive experimental \selectlanguage{greek}γ\selectlanguage{english}[?] \selectlanguage{english}database
covering 215 ILs and 112 solutes (totally 41,553 data points) is
established for training the RS, where the cross-validation and test are
performed based on a rigorous dataset split by IL-solute combinations.
The obtained RS shows superior performance than the state-of-the-art \selectlanguage{greek}γ\selectlanguage{english}[?]
\selectlanguage{english}models and is thus taken to complete the solute-in-IL \selectlanguage{greek}γ\selectlanguage{english}[?] \selectlanguage{english}matrix. Based
on the completed \selectlanguage{greek}γ\selectlanguage{english}[?] \selectlanguage{english}database, a large extension of the UNIFAC-IL model
is finally presented, filling all the parameters between involved
groups.%
\end{abstract}%



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\textbf{Hosted file}

\verb`AICHE-Manuscript-20200715.docx`  available at \url{https://authorea.com/users/344984/articles/471361-neural-recommender-system-for-activity-coefficient-prediction-and-unifac-model-extension-of-ionic-liquid-solute-systems}

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