Polymer chemistry is another field where ML is accommodating. Bayesian models have been successfully developed to aid the discovery of polymers with high thermal conductivity . However, ML models were trained on a substantially limited number of polymeric properties and predicted the QSPR of thermal conductivity and other polymeric properties. Besides Bayesian models, genetic algorithms have been applied to train ML models to design new polymers . The genetic algorithm was trained to predict and design glass transition temperature and the band gap of polymers. The ML model was able to design 132 polymers with desired properties and good accuracy. Thus, genetic algorithms can be generalized to predict and model other property objectives since corresponding reliable property prediction models can be provided.