Language models can learn task-specific representations of chemical reactions directly from text, enabling more efficient multi-objective optimization than hand-crafted chemical descriptors—achieving high-yield synthesis conditions with under 3% of the design space explored.
Researchers developed a method to optimize chemical reactions across multiple goals (yield, selectivity, safety) by using a fine-tuned language model to learn dynamic representations of reaction components from text descriptions.