Machine learning could ‘change the paradigm’ for polaritonic chemistry

Machine learning models have given a never-before-seen glimpse into the intricate and mysterious mechanics of reactions driven by light–matter hybrid interactions. The techniques are a powerful new tool for scientists working in the field of polaritonic chemistry, which explores the interactions between light and matter on a quantum level.

‘Controlling a chemical reaction is quintessential in chemistry, but often we have limited control over the outcome,’ explains computational chemist Christian Schäfer from Chalmers University of Technology in Gothenburg, Sweden. ‘Polaritonic chemistry promises [to let us] steer a reaction on demand.’