LLMs can significantly accelerate scientific model discovery by injecting domain expertise into symbolic regression, cutting experimental iterations roughly in half while maintaining prediction accuracy.
This paper combines symbolic regression with large language models to automatically discover kinetic models in chemistry and bioprocessing. The LLM critiques candidate models and proposes new ones based on chemical knowledge, reducing the number of iterations needed to find correct models by 41-79% compared to standard symbolic regression alone.