The rise of Machine Learning (ML) has been continuously advancing the frontier of materials modeling and multiscale simulations. However, due to expense of physical or numerical experiments, material modelers & designers oftentimes cannot foresee all corner-cases that will arise in real environments, causing failure of most ML models that rely on data interpolation. In this talk, I will introduce our recent efforts on Deep Material Network (DMN), which presents a new way of embedding material physics into a network architecture. Its key features are the physics-based building block with interpretable fitting parameters, extrapolation capability for material and geometric nonlinearities with only linear elastic training data, and efficient online inference. An integration data-driven framework based on transfer learning and concurrent simulation in LS-DYNA will also be discussed.