The fascinating diversity of material behavior at the macroscopic scale, including plasticity, phase transformations, viscoelasticity or diffusion, can only emerge from the underlying atomistic or particle behavior. Yet, the direct connection between these two scales for non-equilibrium phenomena remains an extremely challenging quest from both a theoretical and computational perspective. This knowledge gap currently hinders predictive simulations and material discovery, and leads to large economic losses and barriers to innovation in many industrial sectors. In this talk, we will discuss several advances in the predictive modeling of non-equilibrium mechanics, by combining tools from continuum mechanics, statistical physics, applied mathematics, as well as machine learning and data-driven strategies.