PolyGraphPy: A unified Python framework for atomistic simulation and machine learning-driven polymer design

Abstract

Polymers are indispensable materials with daily applications ranging from electronics to medicine, owing to their versatility, which can be tailored by adjusting their chemical composition and architecture. The design space for these compounds is vast and governed by factors such as monomer classes, copolymer configurations (e.g., linear, branched, random, and alternating), chain size, stoichiometry, and material properties (e.g., density, refractive index, solubility, and Poisson’s ratio). Thus, its exploration requires efficient computational methodologies for polymer science. To address this challenge, this paper introduces PolyGraphPy, an open-source, unified Python framework that integrates atomistic simulations with machine learning for accurate property prediction and property-guided polymer design. The framework automates quantum mechanics calculations using Density Functional Tight Binding (DFTB+) to efficiently construct structured datasets for monomers, homopolymers, and alternating copolymers. For property prediction, PolyGraphPy employs Bayesian Graph Neural Networks (GNNs) utilizing stochastic graph representations to predict target properties—such as static polarizability, while providing robust uncertainty quantification. Furthermore, the platform incorporates two complementary generative models for the de novo design of targeted molecules: a SELFIES-based Generative Pretrained Transformer (GPT) and a Genetic Algorithm (GA) based on BRICS graph fragmentation. Demonstrated on a dataset of acrylates, PolyGraphPy provides a highly customizable, end-to-end pipeline that reduces computational costs and accelerates data-driven polymer informatics.