Open-source Polymer Generative Pipeline

TL;DR

This paper introduces an open-source computational pipeline that combines neural networks and filtration mechanisms to generate novel polymers with targeted properties, expanding the experimental possibilities in polymer research.

Contribution

It presents a novel, integrated generative pipeline for polymers that improves upon existing models and is accessible within the DeepChem framework.

Findings

Discriminators outperform state-of-the-art models.

Generators produce polymers with desired ionization potential.

Pipeline enhances polymer design capabilities.

Abstract

Polymers play a crucial role in the development of engineering materials, with applications ranging from mechanical to biomedical fields. However, the limited polymerization processes constrain the variety of organic building blocks that can be experimentally tested. We propose an open-source computational generative pipeline that integrates neural-network-based discriminators, generators, and query-based filtration mechanisms to overcome this limitation and generate hypothetical polymers. The pipeline targets properties, such as ionization potential (IP), by aligning various representational formats to generate hypothetical polymer candidates. The discriminators demonstrate improvements over state-of-the-art models due to optimized architecture, while the generators produce novel polymers tailored to the desired property range. We conducted extensive evaluations to assess the generative performance of the pipeline components, focusing on the polymers' ionization potential (IP). The developed pipeline is integrated into the DeepChem framework, enhancing its accessibility and compatibility for various polymer generation studies.