  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.
