Prediction of excess enthalpy in binary mixtures through probabilistic matrix completion, with a GP enforced smoothness constraint

02 June 2025, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

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.

Keywords

Matrix completion
excess enthalpy
thermophysical property
pseudodata
mixture data
machine learning
prediction

Supplementary materials

Title
Description
Actions
Title
Supporting Information
Description
Contains limitation of formulating a Bayesian framework for this study, error metrics for all testing mixtures and the references for all data sourced.
Actions

Supplementary weblinks

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.