.png){kind=icon}
返回
PolyCL: contrastive learning for polymer representation learning via explicit and implicit augmentations
.png){kind=icon}
OA
AI
DOI：10.1039/d4dd00236a.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 robust and 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.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.6
论文数:
997
被引数:
1.7K
机构
引用论文
.png){kind=icon}
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules微笑，一种化学语言和信息系统。1.介绍方法和编码规则
PREAI
分享收藏Interlinked Polyaniline/ZnO Nanorod Composite for Selective NO2 Gas Sensing at Room Temperature
PREAI
分享收藏
查看更多内容