Researchers from the Skoltech Materials Center have proposed a new approach to modeling the mechanical properties of heterogeneous materials, combining machine learning with active learning on local chemical configurations. The method enables calculations of large systems containing tens of thousands of atoms with accuracy comparable to direct quantum-mechanical calculations (DFT), but without requiring extensive computational resources.