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arXiv Machine Learning By Joshua W. Sin, David Ming Segura, Bojana Rankovi\'c, Siu Lun Chau, Marius D. R. Lutz, Andrea Anelli, Ryan P. Burwood, Kurt P\"untener, Maximilian J. Notheis, Raphael Bigler, Philippe Schwaller

Dynamic language model representations for multi-objective reaction optimisation

Read the original on arXiv Machine Learning →

The paper introduces a method that learns dynamic reaction representations directly from textual descriptions using a fine‑tuned language model coupled with Gaussian process surrogates. This approach enables multi‑objective Bayesian optimisation for chemical reactions, achieving faster convergence than traditional descriptor libraries or one‑hot encodings across nickel‑, palladium‑, and iridium‑catalysed systems. Prospective experiments on a palladium‑catalysed cyanation and an asymmetric hydrogenation produced high‑yield, high‑enantiomeric‑excess conditions after only two rounds of high‑throughput testing, translating directly to gram‑scale synthesis.

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