Thermoset nanocomposites (TSNCs) may offer significantly improved performance over conventional thermoset materials, and thus are attractive for wide industrial applications, especially in the coating industry. Design of TSNCs via experiment, however, faces various technical challenges due to design complexity. Computational design can provide deep insights and identify superior design solutions through exploring opportunities in a usually huge design space. This paper introduces a generic computational methodology for the design, characterization, and testing of TSNC-based coatings. A distinct feature of the methodology is its capability of generating quantitative correlations among material formulation, processing condition, coating microstructure and property, coating performance, and processing efficiency. The correlations can enable a comprehensive analysis for optimal TSNC coating design. Case studies will demonstrate the methodological efficacy and attractiveness. (C) 2009 Elsevier Ltd. All rights reserved.