Artificial intelligence can whip up the formula to recreate a perfume based on its chemical composition. One day, it could use a lone sample to reproduce rare smells at risk of being lost, such as incense from a culturally specific ritual or the smell of a forest that is changing because of rising temperatures.

Idelfonso Nogueira at the Norwegian University of Science and Technology and his colleagues profiled two existing fragrances, categorising them by scent family – subjective words such as “spicy” or “musk” commonly used to describe perfume – and so-called “odour value”, a measure of how intense a certain smell is. For instance, one of the fragrances scored the highest odour value for “coumarinic”, a family of scents similar to vanilla. The other received the highest odour value for the scent family “alcoholic”.

To train a neural network, the researchers used a database of known molecules associated with specific fragrance notes. The AI learned to generate an array of molecules that matched the odour scores for each scent family of the sample fragrances.

But merely generating those molecules was not enough to reproduce the target fragrances, says Nogueira, because the way we perceive smell is affected by the physical and chemical processes molecules go through when they interact with air or skin. Immediately after being sprayed, a perfume’s “top notes” are most noticeable, but they vanish within minutes as molecules evaporate, leaving “base notes” that can linger for days. To address this, the team chose molecules generated by the AI that evaporated under similar conditions as those in the original fragrances.

Finally, they again used AI to minimise any mismatches between the odour values of the original mixture and the AI-generated mixture. Their ultimate recipe for one of the fragrances showed small deviations with respect to its “coumarinic” and “sharp” notes, while the other seemed to be a very precise replica.

Predicting what a chemical will smell like is notoriously difficult, so the researchers used a limited number of molecules in their training data. But the process could be even more precise if the database is expanded to contain more – and more complex – molecules, says Nogueira. He suggests AI could help the perfume industry create recipes that produce a cheaper, more sustainable version of a fragrance. Currently, experts estimate developing a new perfume with traditional techniques can take up to three years and cost as much as $50,000 per kilogram.

Richard Gerkin at Arizona State University and Osmo, a start-up aiming to teach computers how to generate smells like AI can do with images, says combining AI with physics and chemistry is a strength of this approach because it accounts for often overlooked subtleties such as how smells evaporate. But the effectiveness of this process still has to be confirmed in studies with people, he says.

Nogueria and his colleagues have already nearly gotten there. In a few weeks, he will be off to a colleague’s lab in Ljubljana, Slovenia, to experience some of the AI-generated fragrances himself. “I am very excited to smell them,” he says.