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Optimizing toward Discovery: AI-Driven Exploration of Lewis Acid–Base Catalysts for PET Glycolysis
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An AI-driven Bayesian optimization framework combined with large language model reasoning and robotic high-throughput experimentation identified a zinc pivalate/diamine catalyst achieving 95% PET depolymerization yield in just 20 minutes. Efficient chemical recycling of PET — one of the most commonly detected microplastic polymers in the environment — is a critical upstream intervention for reducing microplastic generation at scale.
The depolymerization of polyethylene terephthalate (PET) through efficient chemical recycling remains a central challenge in plastic waste valorization, in part because the catalyst landscape is vast and sparsely explored. Here, we present an artificial intelligence (AI)-driven discovery framework that integrates Bayesian optimization (BO), large language models (LLMs), and high-throughput robotics to accelerate the search for Lewis acid-base catalysts for PET glycolysis. Starting from a literature-guided baseline, BO used LLM-derived semantic embeddings of chemical knowledge to navigate a high-dimensional space of 11,160 candidate pairs, identifying promising candidates beyond the initial state of the art. The LLM then analyzed the experimental results to generate interpretable, data-driven hypotheses that guided further experiments and enabled inductive, human-led extrapolation beyond the predefined search space. This workflow yielded a zinc pivalate/N,N'-diethylethylenediamine catalyst delivering 95% bis(2-hydroxyethyl) terephthalate (BHET) yield in 20 min, with robust performance upon scale-up and on postconsumer PET. Mechanistic analysis supports a synergistic dual-site activation mode and informs transferable design principles. All experiments were executed on a fully autonomous AI-Chemist platform with automated reaction setup and nuclear magnetic resonance (NMR) spectroscopic analysis. Together, these results show how automation-AI-human collaboration can progress from optimization to out-of-sample discovery in large, underexplored chemical spaces.
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Scientists used AI to design a super-enzyme that can break down PET plastic (the material in water bottles and clothing) into its original building blocks, even without needing to pre-treat or heat-soften the plastic first—something that's been a major roadblock for plastic recycling. This matters because PET plastic pollution takes centuries to break down naturally and sheds microplastics into our food, water, and bodies; this enzyme could enable truly circular recycling where old plastic becomes new, high-quality plastic again instead of ending up in landfills, oceans, or our bloodstreams.
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