Abstract: The modeling of macroscopic properties of materials often require to accurately evaluate physical quantities at several time and length scales.Here we show how symbolic inference, i.e., the machine learning of simple analytical expressions that explain and generalize the available the data, can effectively bridge physical scales. The focus is on learning models that are as simple as possible (but not simpler...), with as few as possible data points. I will demonstrate the application of the methods to the modeling of catalytic properties of materials, thermal conductivity, and more.