The Jayaraman research group members’ expertise lies in development of coarse-grained models and computational approaches involving liquid state theory, molecular simulation, and machine learning for designing and characterizing macromolecular materials. I will share results from one specific project to highlight how we use these computational methods in combination with experiments from our collaborators. I will also introduce in this talk a method we recently developed called ‘Computational Reverse Engineering of Scattering Experiments (CREASE)’ that is useful for analysis of small angle scattering profiles and interpretation of the assembled structure in macromolecular solutions. CREASE is comprised of two steps: the first step involves a genetic algorithm (GA) to determine the shape and dimensions of the domains in the assembled structure and the second step uses molecular simulations to reconstruct chain conformations and monomer level arrangements within the assembled structure. We validate the GA step within CREASE by taking input scattering intensity profiles from a variety of assembled shapes with known shapes and dimensions, and by producing outputs that match those known shapes and target dimensions. Besides applying CREASE to scattering data where the shape is known, I will demonstrate how CREASE would be applied to experimental scattering profiles and realistic situations where microscopy may hint at the potential shapes without the user knowing the dimensions with certainty, to test hypotheses on the shape and calculate the resulting dimensions for that shape. CREASE’s power lies in its ability to interpret structural detail at a range of length scales for macromolecular solutions without relying on fitting with off-the-shelf analytical models that may be too approximate for novel polymers and/or unconventional assembled structures.