Credit: KAIST

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A KAIST research team has introduced a new technique that combines physical laws, which govern deformation and interaction of materials and energy, with artificial intelligence. This approach allows for rapid exploration of new materials even under data-scarce conditions and provides a foundation for accelerating design and verification across multiple engineering fields, including materials, mechanics, energy, and electronics.

Professor Seunghwa Ryu’s research group in the Department of Mechanical Engineering, in collaboration with Professor Jae Hyuk Lim’s group at Kyung Hee University and Dr. Byungki Ryu at the Korea Electrotechnology Research Institute, proposed a new method that can accurately determine material properties with only limited data. The method uses physics-informed machine learning (PIML), which directly incorporates physical laws into the AI learning process.

In the first study, the researchers focused on hyperelastic materials, such as rubber. They presented a physics-informed neural network (PINN) method that can identify both the deformation behavior and the properties of materials using only a small amount of data obtained from a single experiment. Whereas previous approaches required large, complex datasets, this research demonstrated that material characteristics can be reliably reproduced even when data is scarce, limited, or noisy.

In the second study, the team turned to thermoelectric materials—new materials that convert heat into electricity and electricity into heat. They proposed a PINN-based inverse inference technique that can estimate key indicators, such as thermal conductivity (how well heat is transferred) and the Seebeck coefficient (how efficiently electricity is generated), from just a few measurements.

Going further, the researchers introduced a Physics-Informed Neural Operator (PINO), an AI model that understands the physical laws of nature, and showed that it can generalize to previously unseen materials without requiring retraining.

In fact, after training the system on 20 materials, they tested it on 60 entirely new materials, and in all cases it predicted their properties with high accuracy. This breakthrough points to a future where large-scale, high-speed screening of countless candidate materials becomes possible.

This achievement goes beyond simply reducing the need for experiments. By intricately combining physical laws with AI, the researchers provided the first example of improving experimental efficiency while preserving reliability.

Professor Seunghwa Ryu, who led both studies, stated, “This is the first case of applying AI that understands physical laws to real material research. It enables reliable identification of material properties even when data availability is limited, and it is expected to expand into various engineering fields.”

The first paper, co-first-authored by KAIST Mechanical Engineering Ph.D. candidates Hyeonbin Moon and Donggeun Park, is published in Computer Methods in Applied Mechanics and Engineering.

The second paper, co-first-authored by KAIST Mechanical Engineering Ph.D. candidates Hyeonbin Moon and Songho Lee, and Dr. Wabi Demeke, is published in npj Computational Materials.

More information: Hyeonbin Moon et al, Physics-informed neural network-based discovery of hyperelastic constitutive models from extremely scarce data, Computer Methods in Applied Mechanics and Engineering (2025). DOI: 10.1016/j.cma.2025.118258

Hyeonbin Moon et al, Physics-informed neural operators for generalizable and label-free inference of temperature-dependent thermoelectric properties, npj Computational Materials (2025). DOI: 10.1038/s41524-025-01769-1

Citation: Physics-informed AI excels at large-scale discovery of new materials (2025, October 10) retrieved 10 October 2025 from https://phys.org/news/2025-10-physics-ai-excels-large-scale.html

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