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多聚体 BERT:一种化学语言模型,能够实现完全由机器驱动的超快聚合物信息学。

polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics.

机构信息

School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

Faculty of Engineering Science, University of Bayreuth, 95447, Bayreuth, Germany.

出版信息

Nat Commun. 2023 Jul 11;14(1):4099. doi: 10.1038/s41467-023-39868-6.

Abstract

Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures.

摘要

聚合物是日常生活中不可或缺的一部分。它们的化学世界非常庞大,这为识别特定应用的合适候选物提供了前所未有的机遇和巨大的挑战。我们提出了一个完整的端到端机器驱动的聚合物信息学管道,能够以空前的速度和准确性在这个空间中搜索合适的候选物。这个管道包括一个叫做 polyBERT 的聚合物化学指纹识别能力(受自然语言处理概念的启发),以及一种多任务学习方法,将 polyBERT 指纹映射到一系列性质上。polyBERT 是一个化学语言学家,它将聚合物的化学结构视为一种化学语言。与基于手工指纹方案的现有最佳聚合物性质预测方法相比,本方法在速度上提高了两个数量级,同时保持了准确性,因此成为包括云基础设施在内的可扩展架构中部署的有力候选物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4c09/10336012/a7b830a173c0/41467_2023_39868_Fig1_HTML.jpg

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