Abstract: A method for designing polymers includes translating polymer representations of a training dataset and a test dataset into a format comprehensible by a generative pretraining transformer (GPT)-based model, training the GPT-based model with the translated polymer representations, generating new polymer representations, in a predefined format, using the trained GPT-based model, predicting at least one property of the generated new polymer representations using a machine learning (ML) property predictive model and selecting a first subset of the generated new polymer representations as a function of the at least one predicted property, and calculating the at least one property of the first subset of the generated new polymer representations using a molecular dynamics (MD) module.

Abstract: A method of closed loop simulation for accelerated material discovery is described. The method includes ranking a plurality of candidate systems according to corresponding properties of interest predicted by a first prediction model. The method also includes simulating a first top-N of the plurality of candidate systems according to the corresponding properties of interest predicted by the first prediction model. The method further includes re-ranking the plurality of candidate systems according to the corresponding properties of interest predicted by a second prediction model. The method also includes simulating a second top-N of the plurality of candidate systems according to the corresponding properties of interest predicted by the second prediction model.