An AI capable of doubt can optimize scientific discovery

02.09.26 - A team at EPFL have developed a framework that trains Large Language Models to create AI systems that can find the best possible setup or “recipe” for a scientific experiment. Modern computational tools let scientists explore huge numbers of possible molecules, materials, and chemical reactions. But testing every combination in the lab is slow and costly. So how do researchers choose the best “recipe” for their experiment? One method called “Bayesian optimization” learns from previous results, predicts which options look promising, and estimates how uncertain those predictions are. This allows researchers to focus their next experiments on the most useful options to test and avoid wasting time and money on less promising possibilities. Nevertheless, this method doesn’t carry over well between scientific fields. For example, an efficient model for choosing chemical reactions might not be suited to materials or molecular design, so each new problem effectively starts from scratch. The rise of Large Language Models (LLMs) has opened up another route. LLMs already encode broad scientific knowledge and work with information expressed as text. But they too have a weakness: hal