Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges

TL;DR

This paper explores how graph neural networks can help predict polymer properties, but highlights challenges like limited data and the need for collaboration.

Contribution

The paper provides a comprehensive overview of graph neural networks for polymer characterization and identifies key challenges in the field.

Findings

Graph neural networks show promise for accelerating polymer property prediction.

Current challenges include insufficient datasets and complex polymer configurations.

Collaborative efforts like CRIPT aim to address data and standardization issues.

Abstract

Using machine learning to accelerate the characterization
and prediction
of properties of many-molecule systems, such as polymers, is appealing,
yet challenging. Polymers are large, complex molecules that have unique
properties and potential applications in a wide range of industries.
Their potential in advancing fields such as ion-transport polymer
for energy storage, lightweighting of structural materials, bioinspired
multifunctional materials, etc., provide enough impetus for accelerating
the discovery of novel polymeric materials. However, mathematical
mapping and the consequent manipulation of polymer structures are
still challenging tasks due to their complex configuration and the
smorgasbord of motifs encountered naturally and in engineering materials.
Traditional methods of polymer structure mapping and property prediction
at multiscale domains can include approaches such as Density Functional
Theory, Molecular Dynamics, and Finite Element Analysis, which can
be time-consuming and computationally expensive. The promise of machine
learning to accelerate these tasks is appealing, and currently, researchers
are pursuing the development of architectures and composition approaches
to accomplish this. Here we discuss the current state of the knowledge
on the use of Graph Neural Networks, and related architectures, being
developed and/or used for the characterization and prediction of properties
of polymers. Many challenges still exist such as the lack of sufficient
and comprehensive data sets. To address these issues, efforts are
being pursuedsuch as the so-called CRIPT (Community
Resource for Innovation in Polymer Technology) led by a lab consortium
that includes representations from private industry, academia, government,
and others. We conclude that even though this field is young it has
both momentum and promise. The current challenges that must be overcome
are also addressed.

Figures