Density of polymeric materials
Case Study Provider:
Institute of Science Tokyo
Overview
Polymer materials have a wide variety of applications, which include optical devices such as thin lenses, etc. When designing molecules for such optical applications, it is necessary to predict the refractive index because the property is important for glass materials. However, the calculation cost increases significantly when intermolecular interactions and packing states are taken into account. Therefore, we investigated a method to predict the refractive index by calculating the density of polymers using PFP.
Calculation models and methods
As an example of a density calculation, we targeted the unit structure of a polymer. First, we extracted about 20 molecules from that unit structure and placed them in a simulation cell.
Next, we performed structural optimization using PFP and estimated the density from the results. To check the reproducibility of the results, we randomly generated 10 models with different initial arrangements of molecules and performed the same calculations on each model.
Calculation Condition
Calculation Results and Discussion
Densities were calculated for about 40 polymers with high refractive index included in the Polymer Database. As a result, the correlation coefficient between the calculated density and the measured value was 0.93, which is a high agreement (upper figure left). This value was better than the value calculated assuming a single molecule in vacuum using the RDKit module.
On the other hand, the predicted values for this method were generally lower than the measured values. This may be due to the fact that the molecules were not modeled as polymer chains in the simulations. To solve this problem, we performed an additional simulation of the packing state using an oligomer consisting of five linked units and confirmed that the systematic error almost disappeared (upper figure right).
In the future, we will continue our search for polymers with high refractive indices by taking advantage of PFP’s high accuracy, speed, and versatility, and we believe that PFP is becoming a powerful tool not only for this application but also for entering the hierarchical structure of organic materials.
