Abstract
Matrix completion (MC) methods have found success in application to the prediction of scalar valued thermophysical properties, and more recently for mixture data in our own group. In this work we employ MC using the probabilistic matrix factorization (PMF) framework to generate pseudo excess enthalpy data, HE, for binary mixtures using data from established thermophysical property databases. We employ Gaussian processes (GP) to enforce smoothness of the pseudo excess enthalpy data across both composition and temperature. The equivalent kernel for the GP was derived from a modified version of the Redlich-Kister polynomial. We incorporate several thermodynamic considerations to improve the accuracy, robustness and interpretability of the estimates obtained, which we propose as surrogates in the absence of experimental data for fundamental thermodynamic model improvement.
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Contains limitation of formulating a Bayesian framework for this study, error metrics for all testing mixtures and the references for all data sourced.
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The project GitHub repository contains all code and library reference required to reproduce the results reported in the manuscript. Any use of the code should reference the final published manuscript (reference to be added on publication).
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