Development of scalable and generalizable machine learned force field for polymers.

ABSTRACT: Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain polymer systems such as polyethylene glycol. As a solution, we present an alternative potential energy surface, a charge recursive neural network (QRNN) model trained on DFT calculations made on smaller atomic clusters that generalizes well to oligomers comprising larger atomic clusters or longer chains. We demonstrate the validity of the polymer QRNN workflow by modeling the oligomers of ethylene glycol. We apply two rounds of active learning (addition of new training clusters based on current model performance) and implement a novel model training appr

SUBMITTER: Mohanty S 

PROVIDER: S-EPMC10567837 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature