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.

翻译：聚合物因其多样且可调控的特性，在广泛的应用领域中发挥着至关重要的作用。建立聚合物表征与其性质之间的关系，对于通过机器学习进行潜在聚合物的计算设计与筛选至关重要。表征的质量显著影响着这些计算方法的有效性。本文提出了一种自监督对比学习范式——PolyCL，用于在无需标签的情况下学习高质量的聚合物表征。我们的模型结合了显式和隐式增强策略，以提升学习性能。结果表明，作为一个特征提取器，我们的模型在迁移学习任务上取得了更好或极具竞争力的性能，且无需过于复杂的训练策略或超参数优化。为了进一步提升模型效能，我们对对比学习中使用的多种增强组合进行了广泛分析，从而确定了最大化PolyCL性能的最有效组合。