New Polymers In Silico Generation and Properties Prediction

Abstract

We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smallest repeating unit (SRU) for fast and adequate prediction of polymer properties. We have developed the programs (a) GenStruc, for generating the new polymer SRUs using the enumeration and Monte Carlo algorithms, and (b) PolyPred, for predicting properties for a given input polymer as well as for multiple structures stored in the database files. The structure database from the original Bicerano publication is used to create databases of backbones and pendant groups. A database of 5,142,153 unique SRUs is generated using the scaffold-based combinatorial method. We show that using only known backbones of the polymer SRU and varying the pendant groups can significantly improve the predicted extreme values of polymer properties. Analysis of the obtained results for the dielectric constant and refractive index shows that the values of the dielectric constant are higher for polyhydrazides than for polyhydroxylamines. The high value predicted for the refractive index of polythiophene and its derivatives is in agreement with the experimental data.

Keywords

• polymers
• quantitative structure–property relationships
• combinatorial libraries
• Monte Carlo
• InChIkey
• hits search