Language models dynamically learn chemical reaction representations for optimization

Researchers have developed a novel method for optimizing chemical reactions by dynamically learning representations from text using fine-tuned language models. This approach, integrated with Gaussian processes and Bayesian optimization, adapts representations to specific reaction systems, outperforming traditional methods like one-hot encoding and molecular descriptors. The technique was successfully applied to optimize palladium-catalyzed cyanations and asymmetric hydrogenations, achieving high yields and enantiomeric excess in prospective experiments. AI

IMPACT This research demonstrates a novel application of language models in scientific discovery, potentially accelerating chemical synthesis and materials science.

RANK_REASON Academic paper describing a novel method for reaction optimization using language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• Bayesian optimization
• CatalyzeX
• cyanation
• DagsHub
• Gaussian process
• Gotit.pub
• Hugging Face
• iridium
• language model
• nickel cation binding
• Palladium Reagents and Catalysts
• ruthenium
• ScienceCast

• paper
• model release

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