Among experimental methods, dynamic mechanical analysis (DMA) is a standard technique for quantitative analysis of cross-linked polymers, e.g., measuring cross-link density (XLD). However, in the case of complex polymer networks, the measurement of XLD using DMA can exhibit significant inaccuracies because the relationship between the elastic modulus and XLD may not fully capture the behavior of these structures. The distribution and presence of defects in the network significantly change the thermomechanical properties, especially in complex systems. More advanced techniques, such as network disassembly spectrometry and multiple-quantum NMR, are available for quantifying defects; however, like many other experimental and theoretical methods, they primarily focus on systems involving end-link prepolymers and cannot be generalized to networks made from prepolymers and cross-linkers with diverse functionalities and connecting sites along the chains. Therefore, analyzing complex networks solely through experimentation and theoretical approaches is challenging, and molecular modeling can be an effective complementary tool. Molecular dynamics (MD) simulation is among the most successful methods for modeling polymer networks through a reactive scheme. Various researchers employed methods to construct a reliable network structure, able to predict many properties that were in good agreement with the experimental results. Among those, coarse-grained (CG) methods are superior to atomistic simulations because their soft-core potentials allow for higher mobility of reacting species, enabling reaching high conversions, comparable to experimental synthesis, and covering relatively long reaction times, while still retaining essential molecular information. Nonetheless, despite the advantages of CG models for constructing close-to-equilibrium polymer networks, they are not suitable for accurate investigation of mechanical, thermal, and diffusion properties, which heavily depend on atomistic interactions. Therefore, reverse-mapping the CG crosslinked structures to atomistic models and further equilibration at the atomistic scale are key to accurately assessing these properties. Using a recently developed polymerization package (PolySMart), and adopting a reverse-mapping methodology (), this paper aims to introduce a more comprehensive network characteristic for complex crosslinked polymers formed from multifunctional prepolymer chains and cross-linkers, such as those found in acrylic-melamine. We demonstrate that standard parameters like reaction conversion and XLD lack the accuracy and overlook network features needed to predict thermo-mechanical properties. Accordingly, we introduce a single network parameter by modifying XLD, namely XLD eff , which directly links the microstructure and thermo-mechanical properties. Furthermore, the results of our multi-scale simulationsvalidated against experimental data-explore the role of functionality and prepolymer chain length at various stages of cross-linking.