Imagine scanning a spectrum of an unknown molecule and receiving a few suggestions from an AI assistant as to what it could be. Researchers at MIT and IBM are actively working on this technology and have already achieved powerful results. ‘Those who don’t embrace AI may find themselves left behind.’

When faced with the challenge of interpreting analytical spectra, a common question echoes throughout many laboratories: ‘What in the world am I looking at?’ Fortunately, recent developments in AI, machine learning, and language models have encouraged scientists to tackle this problem using these tools. For instance, new multimodal datasets integrate different spectroscopic data, enabling machine learning models to predict molecular structures more accurately. Other examples include models that transform infrared (IR) or mass spectra into molecular structures.

Notable efforts can be found at IBM, where Teodoro Laino, a distinguished research scientist, and PhD student Marvin Alberts are working on language models. ‘We were among the first to use such models for scientific tasks around eight years ago, and we ultimately arrived at the use case of solving analytical spectra, on which Marvin works.’ Their main goal? Structure elucidation. ‘After a brief synthesis during my master’s degree, it took me over a month to carry out all the necessary measurements and characterisations’, explains Alberts. ‘So during my PhD, we’re aiming to automate this process.’