PolyCL: contrastive learning for polymer representation learning via explicit and implicit augmentations.

Autor:	Zhou J; Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus, Wood Lane London W12 0BZ UK k.jelfs@imperial.ac.uk., Yang Y; Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus, Wood Lane London W12 0BZ UK k.jelfs@imperial.ac.uk., Mroz AM; Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus, Wood Lane London W12 0BZ UK k.jelfs@imperial.ac.uk.; I-X Centre for AI in Science, Imperial College London White City Campus, Wood Lane London W12 0BZ UK., Jelfs KE; Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus, Wood Lane London W12 0BZ UK k.jelfs@imperial.ac.uk.
Jazyk:	angličtina
Zdroj:	Digital discovery [Digit Discov] 2024 Nov 28. Date of Electronic Publication: 2024 Nov 28.
DOI:	10.1039/d4dd00236a
Abstrakt:	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. Competing Interests: There are no conflicts of interest to declare. (This journal is © The Royal Society of Chemistry.)
Databáze:	MEDLINE
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