2023-07-18 ジョージア工科大学
◆polyBERTは、ポリマーの化学構造とつながりを化学的な言語として扱い、自然言語処理に触発された最新の技術を使用して有益な情報を抽出します。このツールは非常に高速であり、従来の指紋法と比べて100倍以上も速くなります。さらに、polyBERTは複数のポリマー特性を同時に予測することができ、データ内の隠れた相関関係を活用して精度を向上させます。このモデルを使用して生成された広範なデータセットは、ポリマーの宇宙を探索し、新たな発見や実用的な応用を可能にします。
＜関連情報＞
- https://research.gatech.edu/revolutionary-ai-algorithm-learns-chemical-language-and-accelerates-polymer-research
- https://www.nature.com/articles/s41467-023-39868-6
polyBERT：機械駆動の超高速ポリマーインフォマティクスを実現する化学言語モデル polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
Christopher Kuenneth & Rampi Ramprasad
Nature Communications Published:11 July 2023
DOI:https://doi.org/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.
