Refractive index prediction models for polymers using machine learning

The refractive index (RI) is an important material property and is necessary for making informed materials selection decisions when optical properties are important. Acquiring accurate empirical measurements of RI is time consuming, and while semi-empirical and computational determination of RI is generally faster than empirical determination, predictions are less accurate. In this work, we utilized experimentally measured RI data of polymers to build a machine learning model capable of making accurate near-instantaneous predictions of RI. The Gaussian process regression model is trained using data of 527 unique polymers. Feature engineering techniques were also used to optimize model performance. This new model is one of the most chemically diverse and accurate RI prediction models to date and improves upon our previous work. We also concluded that the model is capable of providing insights about structure-property relationships important for estimating the RI when designing new polymer backbones.