Researchers gave an AI data from physics experiments involving systems using pendulum-like motion to see if it could derive basic laws of physics.Credit: stefilyn/Getty
Most artificial-intelligence (AI) models can reliably identify patterns in data and make predictions, but struggle to use that data to come up with broad scientific concepts, such as the laws of gravity. Now, a team in China has developed a system called AI-Newton that, after being fed experimental data, can autonomously ‘discover’ key physics principles, such as Newton’s second law describing the effect of force and mass on acceleration.
The model mimics the human scientific process by incrementally building a knowledge base of concepts and laws, says Yan-Qing Ma, a physicist at Peking University in Beijing who helped to develop the system. Being able to identify useful concepts means that the system can potentially discover scientific insights without human pre-programming, Ma adds.
Keyon Vafa, a computer scientist at Harvard University in Cambridge, Massachusetts, explains that AI-Newton uses an approach called symbolic regression, in which the model hunts for the best mathematical equation to represent physical phenomena. This technique is a promising method for scientific discovery, he adds, because the system is programmed in a way that encourages it to deduce concepts.
The team at Peking University used a simulator to generate data from 46 physics experiments1 involving the free motion of balls and springs, collisions between objects and the behaviour of systems exhibiting vibrations, oscillations and pendulum-like motion. The simulator also deliberately introduced statistical errors to mimic real-world data.
For example, AI-Newton was given data on the position of a ball at a given time and asked to come up with a mathematical equation that explains the relationship between the two variables of time and position. It was able to provide an equation for velocity. It stored this knowledge for the next set of tasks, in which it successfully derived the mass of the ball using Newton’s second law. The results have not yet been peer reviewed.
Planetary trajectories
Scientists have previously used AI models to predict planetary orbits. In 2019, researchers at the Swiss Federal Institute of Technology (ETH) in Zurich developed ‘AI Copernicus’, a neural network that used Earth-based observations to derive formulae for planetary trajectories. In that case, humans were needed to interpret the equations and understand how they relate to the movement of the planets around the Sun.
Vafa and his colleagues at the Massachusetts Institute of Technology in Cambridge, tried a similar experiment with several foundation models, a type of AI model trained on vast data sets, including large language models such as GPT, Claude and Llama.
They trained the models to predict the location of planets across solar systems and then asked them to predict the forces that govern planetary trajectories. In a preprint2, the researchers showed that when the models were trained on orbital trajectories, they could not use the knowledge to do any task other than predicting planetary trajectories. When trying to turn that orbital trajectory data into a law about how forces behave, the foundation AI models derived a nonsensical law of gravitation.