02.09.2026
Artificial Intelligence Suggests New Physics Experiments
AI has long played an important role in research. Now, an international research team has shown that artificial intelligence can even be remarkably useful in designing new experiments.
Research means asking questions of the universe. For centuries, clever minds have advanced science by devising ingenious experiments designed so that their results reveal something about the laws of nature as clearly and unambiguously as possible.
An international research team has now asked: Can this process be automated? Can artificial intelligence develop new ideas for experiments? The answer is a clear yes. In various areas of physics, AI can propose experiments that enable more precise results than experiments designed by humans. In the journal *Nature*, the team has now presented the current state of this new approach to research.
The Best Experiment from Existing Components
This is a typical situation in experimental physics: You have a laboratory full of equipment — perhaps lasers, lenses and mirrors, perhaps different detectors and electronic components. All of these can be combined in an almost incomprehensible number of ways. And from this vast range of experimental possibilities, you have to select one that can provide new insights into the universe.
Normally, this requires intuition and a great deal of experience. But sometimes even that is not enough, as Mario Krenn discovered. Today, he is Professor of Machine Learning in Science at the University of Tübingen. As a student in Vienna, he was working on the setup for a quantum experiment. But neither he nor the other members of his research group could find a suitable experimental configuration capable of demonstrating the desired quantum effects.
So Mario Krenn decided to ask the computer. He described the individual components available to him mathematically, then had an algorithm search for combinations of these components that would result in a meaningful experiment.
“Programming it only took a few hours. Then I went home and left the computer running,” says Mario Krenn. “When I came into the office the next day, the program had produced a file containing a proposed solution. Of course, that was extremely exciting. I immediately started analysing the proposal, and indeed: unlike all of us, the computer had found an experimental setup that satisfied the necessary criteria.”
A Search Problem, Not a Chatbot
This approach has little in common with the kind of AI familiar from large language models. Chatbots are trained on enormous amounts of data and then generate solutions that are statistically likely. When searching for new physics experiments, the task is entirely different.
“It is an enormous optimisation problem,” says Krenn. “There is an overwhelmingly large space of possible experiments that can be built from the available components. The computer has to search this space systematically in order to find the best possible solution.”
“The results are impressive,” says Prof. Philipp Haslinger, head of the Center for Electron Microscopy at TU Wien. “In electron microscopy in particular, we are only now beginning to work systematically with entanglement and new quantum-mechanical microscopy concepts. Human intuition in this area is often still very limited. Artificial intelligence can therefore identify microscope designs that a human would probably never have come up with, but which can produce significantly better images or offer entirely new measurement possibilities.”
This approach has already been used to improve fusion reactors, develop new ideas for particle detectors and generate proposals for making gravitational-wave detector systems even more sensitive.
"Sometimes you look at these computer-generated experimental proposals and quickly understand the idea behind them — why the new concept works better than previous approaches,” says Mario Krenn. “But sometimes it is also very difficult to understand. You can calculate that the new experimental setup works better, but you cannot really put into words why.”
This is possible because modern computers can simulate a wide range of physical situations within a manageable amount of time.
“The goal is to develop something like a universal physics simulator,” says Mario Krenn. “Today, a few important fundamental equations of physics can already take you a very long way. A computer could use them to predict what will happen in a particular experimental setup and then optimise the setup according to the desired objective.”
The Art of Defining the Goal
Defining that objective, however, remains the task of humans.
“That is precisely the challenge: defining as accurately as possible what you actually want, and which constraints have to be satisfied — for example, a maximum cost, or a maximum amount of energy the device can absorb without exploding.”
But isn’t it also a little unfortunate if, in the future, we leave the great eureka moments of science to computers?
“No, absolutely not,” says Mario Krenn. “Human work is simply shifting to a higher level. In the past, calculations had to be done by hand, and nobody wants to go back to that today. Now we have tools that can develop great experimental ideas for us — but using these tools will still require scientific expertise, creativity and a good intuition for physics.”
According to a press release from TU Wien
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