The availability of large databases of mechanical properties occurred at the same time as a growth in interest for data-based models and the increased availability of software libraries for statistical learning. In the past five years, this has lead to the development and publication of machine learning models trained in this data sets obtained at the microscopic level (typically through DFT calculations). The goal there is to identify structure-property relationships and to predict mechanical behavior based on a material's structure, sidestepping any actual atomistic modelling -i.e., without relying on physics-informed molecular simulations.