Elastic Properties of Defective 2D Polymers from Regression Driven Coarse-Graining

Two-dimensional polymers (2DPs) are an interesting class of polymers due to their reticular synthetic assembly, which make them an ideal platform for designing materials with specific target properties. Predicting and understanding their elastic behavior is crucial for their application. However, a realistic calculation of their properties remains computationally challenging due to the ubiquitous presence of defects in synthesized 2DPs. Here, we introduce a coarse-graining (CG) approach based on elastic beams called MikadoRR with parameters extracted from a simple regression-based fitting. This approach allows us to accurately calculate the elastic properties of defective 2DPs up to the microscale. Furthermore, we show that design principles of 2DPs for tailored elastic properties can be derived from this CG model.