Are we rushing ahead with AI in the lab?

The emergence of self-driving labs and automated experimentation has brought with it the promise of increased rates of productivity and discovery in chemistry beyond what we can achieve as humans alone. But the black box nature of AI means we cannot see how or why deep learning systems make their decisions, making it difficult to know how it can best be used to optimise scientific research or if the outcomes can ever be trusted.

In November 2023, a paper was published in Nature reporting the discovery of over 40 novel materials using an autonomous laboratory guided by AI. However, researchers were quick to question the autonomous lab’s results. A preprint followed in January that reported that there were ‘systematic errors all the way through’ owing to issues with both the computational and experimental work.

One of the authors of the critique, Robert Palgrave, a materials chemist at University College London, UK, said that although AI had made ‘big advances’ there was ‘a bit of a tendency’ to feel that AI had to change everything ‘right now’ and that actually we should not expect things to change overnight.

Milad Abolhasani, who leads a research group that uses autonomous robotic experimentation to study flow chemistry strategies at North Carolina State University in the US, says the ‘hype’ has taken over somewhat when it comes to AI and it is time to pause. ‘As humans we are great at envisioning what the future is going to look like and what are the possibilities but … you have to move step by step and make sure things are done correctly.’