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January 24, 2026Journal of the American Chemical Society

Optimizing toward Discovery: AI-Driven Exploration of Lewis Acid–Base Catalysts for PET Glycolysis

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Authors

YYYe YuZXZikai XieMLMan Luo

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Overview

AI-driven framework identifies efficient catalysts for PET glycolysis, highlighting promising dual-site activation mechanisms.

Key Points

  • The aim is to accelerate the discovery of effective Lewis acid-base catalysts for the recycling of polyethylene terephthalate (PET).
  • Utilized artificial intelligence (AI) to guide the experimental search for catalysts.
  • Integrated Bayesian optimization (BO) with large language models (LLMs) and robotics for high-throughput screening.
  • Explored 11,160 candidate pairs based on semantic embeddings from literature.
  • Conducted mechanistic analysis on candidate performance and reaction pathways.
  • Identified a zinc pivalate/N,N'-diethylethylenediamine catalyst that achieved 95% yield of bis(2-hydroxyethyl) terephthalate (BHET) in 20 minutes.
  • Demonstrated robustness on scaled-up reactions and on postconsumer PET.
  • Supported synergistic dual-site activation mechanisms and established transferable design principles.

Cite This Study

Yu et al. (2026) studied this question.

synapsesocial.com/papers/69746187bb9d90c67120b660https://doi.org/10.1021/jacs.5c20630
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