Predicting second virial coefficients of inorganic and organic compounds using Gaussian Process Regression

We show that by using intuitive and accessible molecular features it is possible to predict the temperature-dependent second virial coefficient of organic and inorganic compounds using Gaussian process regression. In particular, we find that a low dimensional representation of features based on intrinsic molecular properties, topology and physical properties relevant for the characterization of molecule-molecule interactions, succeeds to predict the second virial coefficient of any molecule with a relative error $\lesssim 1\% $.