Bayesian optimisation: Using AI to choose your next experiment 

When chemists want to optimize a chemical reaction or a formulation, the most common method they use is “trial and error”.

This involves trying many combinations of parameters, chosen using chemistry knowledge and intuition, until the result meets their requirements. Each experiment brings new knowledge that is useful to design the next experiments.

However, trial and error is a slow methodology that it is limited to a few parameters because the number of experiments grows exponentially with the number of parameters, often fails to yield satisfactory results, and can trap chemists in a “local optimum” from where it is difficult to improve.

Collaboration with Chemistry World

We are excited to announce our new article with Chemistry World, 'SmartChemistry brings the power of AI and machine learning into your lab'

How is AI set to change the way we experiment? Find out in our new blog!

From quicker experimentation to more meaningful analytics for future testing, we discuss how machine learning can improve the process of finding novel molecules now and into the future.

deepmatter® are attending Lab of the Future USA on the 9th and 10th March 2023 in Boston.

Our CPO, Glyn WIilliams will be attending and speaking at ACS Spring 2023 on behalf of deepmatter® on 26-30th March in Indianapolis.

More information on Glyn's talk to come.