PolyCL: Contrastive Learning for Polymer Representation Learning via Explicit and Implicit Augmentations

TL;DR

PolyCL introduces a self-supervised contrastive learning framework that leverages explicit and implicit augmentations to learn high-quality polymer representations, improving transfer learning performance without complex training or hyperparameter tuning.

Contribution

The paper presents a novel contrastive learning approach for polymers that combines augmentation strategies, achieving competitive results without extensive hyperparameter optimization.

Findings

PolyCL outperforms existing methods in transfer learning tasks.

Effective augmentation combinations significantly enhance model performance.

The approach simplifies polymer representation learning without labels.

Abstract

Polymers play a crucial role in a wide array of applications due to their diverse and tunable properties. Establishing the relationship between polymer representations and their properties is crucial to the computational design and screening of potential polymers via machine learning. The quality of the representation significantly influences the effectiveness of these computational methods. Here, we present a self-supervised contrastive learning paradigm, PolyCL, for learning high-quality polymer representation without the need for labels. Our model combines explicit and implicit augmentation strategies for improved learning performance. The results demonstrate that our model achieves either better, or highly competitive, performances on transfer learning tasks as a feature extractor without an overcomplicated training strategy or hyperparameter optimisation. Further enhancing the efficacy of our model, we conducted extensive analyses on various augmentation combinations used in contrastive learning. This led to identifying the most effective combination to maximise PolyCL's performance.