Protein-folding models are the success story in AI for science.

In the late 2010s, researchers from Google DeepMind used machine learning to predict the three-dimensional shape of proteins. AlphaFold 2, announced in 2020, was so good that its creators shared the 2024 Nobel Prize in chemistry with an outside academic.

Yet many academics have had mixed feelings about DeepMind’s advances. In 2018, Mohammed AlQuraishi, then a research fellow at Harvard, wrote a widely read blog post reporting on a “broad sense of existential angst” among protein-folding researchers.

The first version of AlphaFold had just won CASP13, a prominent protein-folding competition. AlQuraishi wrote that he and his fellow academics worried about “whether protein structure prediction as an academic field has a future, or whether like many parts of machine learning, the best research will from here on out get done in industrial labs, with mere breadcrumbs left for academic groups.”

Industrial labs are less likely to share their findings fully or investigate questions without immediate commercial applications. Without academic work, the next generation of insights might end up siloed in a handful of companies, which could slow down progress for the entire field.

Physicist Mario Krenn sees artificial intelligence as a muse — a source of inspiration and ideas for scientists. It’s a description born from his past research and his current work at the Max Planck Institute for the Science of Light, where he and his colleagues develop AI algorithms that can help them learn new ideas and concepts in physics.

His efforts began years ago, when a research team Krenn was part of struggled to come up with an experiment that would let them observe a specific type of quantum entanglement. Krenn, suspecting that their intuition was getting in the way, developed a computer algorithm that can design quantum experiments.

“I let the algorithm run, and within a few hours it found exactly the solution that we as human scientists couldn’t find for many weeks,” he said. Using the blueprint created by the computer, his colleagues were able to build the setup in the laboratory and use it to observe the phenomenon for the first time.

In a subsequent case, the algorithm overcame a barrier by reviving a long-forgotten technique and applying it in a new context. The scientists were immediately able to generalize this idea to other situations, and they wrote about it in a paper for Physical Review Letters.