Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
This addresses the need for accurate prediction of activity coefficients in unexplored binary mixtures, potentially revolutionizing modeling and simulation in chemical engineering.
The authors tackled the problem of predicting activity coefficients in binary liquid mixtures, which are crucial for chemical engineering, by proposing a probabilistic matrix factorization model that outperforms the state-of-the-art method refined over three decades with less training effort.
Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes. Although experimental data on thousands of binary mixtures are available, prediction methods are needed to calculate the activity coefficients in many relevant mixtures that have not been explored to-date. In this report, we propose a probabilistic matrix factorization model for predicting the activity coefficients in arbitrary binary mixtures. Although no physical descriptors for the considered components were used, our method outperforms the state-of-the-art method that has been refined over three decades while requiring much less training effort. This opens perspectives to novel methods for predicting physico-chemical properties of binary mixtures with the potential to revolutionize modeling and simulation in chemical engineering.