AI model overcomes sparse datasets to accurately predict the properties of experimental sustainable aviation fuels and other high-value target molecules.

A novel way to train AI models that empowers them to better assist in cutting-edge research has been developed by researchers at KAUSTarticle.   " id="return-reference-1" href="https://discovery.kaust.edu.sa/en/article/26080/rethinking-machine-learning-for-frontier-science/#reference-1">[1]. The new machine learning method enables accurate AI prediction even in frontier areas of science where only very limited data is available to train the model.

“The new method is already generating new leads in the development of sustainable aviation fuel (SAF), potentially helping to overcome a major challenge in the clean energy transition,” says the lead author of the study, Basem Eraqi, a Ph.D. student in the Clean Energy Research Platform, led by Mani Sarathy.

AI models with property prediction capabilities could dramatically accelerate the discovery of molecules with advanced performance for a specific task. “To build such models, conventional machine learning techniques typically require large, well-balanced datasets to achieve reliable performance,” Eraqi says. However, in many cases — including the development of new pharmaceuticals and polymers, as well as sustainable aviation fuels — there is very little data available for each molecular property of interest.

“Our goal was to develop a machine learning method that performs well even in this ultra-low-data regime, enabling performant material discovery in data-scarce domains,” Eraqi says.

Read more at KAUST Discovery.