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

Area of Science:

• Materials Science
• Polymer Chemistry
• Computational Materials Science

Background:

• Two-dimensional polymers (2DPs) offer tunable properties via reticular synthesis, making them promising for advanced materials.
• Accurate prediction of 2DPs' elastic behavior is vital for applications but hindered by computational challenges due to defects.
• Existing models struggle to realistically simulate the mechanical properties of imperfect 2DPs.

Purpose of the Study:

• To introduce a novel coarse-graining (CG) approach, MikadoRR, for efficient and accurate calculation of defective 2DP elastic properties.
• To validate the CG model's performance against established methods for predicting mechanical behavior.
• To establish a framework for deriving design principles for 2DPs with specific elastic characteristics.

Main Methods:

• Developed MikadoRR, a CG model utilizing elastic beams with parameters derived from regression-based fitting.
• Applied the MikadoRR model to simulate and calculate the elastic properties of 2DPs containing various defects.
• Validated the model's accuracy for predicting properties at the microscale.

Main Results:

• The MikadoRR CG model accurately calculates the elastic properties of defective 2DPs up to the microscale.
• The model demonstrates efficiency compared to traditional atomistic simulations for large-scale 2DP analysis.
• Key design principles for tailoring the elastic behavior of 2DPs were successfully derived from the CG model.

Conclusions:

• MikadoRR provides a computationally efficient and accurate method for evaluating the elastic properties of defective 2DPs.
• The developed CG approach facilitates the rational design of 2DP materials with targeted mechanical performance.
• This work bridges the gap between synthesis, characterization, and application of 2DPs by enabling property prediction.