Paint is designed to offer various chemical and physical properties for surface protection, styling, and appearance. Nevertheless, the anticipated quality of the surface coating is frequently unsatisfactory, which is often attributed to paint formulation. As new demands on coating performance continuously emerge, paint formulation design becomes much more challenging than ever. It is recognized that paint design can be significantly improved with the help of advanced computational methods, as they can provide great freedom and control over the investigation of paint formulation through any number of in silico experiments virtually under any application conditions. This article introduces a lattice Monte Carlo based computational methodology for paint formulation design. By this methodology and structural analysis techniques, a variety of correlations among paint material, curing condition, coating microstructure, and coating qualities can be generated, which are critical for the development of superior paint formulations. A comprehensive study on acrylic-melamine-based paint design and analysis demonstrates the methodological efficacy. (C) 2008 American Institute of Chemical Engineers AIChE J, 55: 132-149, 2009