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

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.