鞐瓣惮/靷绊暀
狠狠撸 Research 1000
| Seung-Hoon Lee | Develops Physics-Based AI Analysis Technology | |||
| 鞛戩劚鞛� | 雽赌鞕疙檷氤挫劶韯� | 鞛戩劚鞚� | 2025-08-06 |
| 臁绊殞靾� | 82 | ||
| Seung-Hoon Lee | Develops Physics-Based AI Analysis Technology | |||||
 |
雽赌鞕疙檷氤挫劶韯� |  |
2025-08-06 |  |
82 |
狠狠撸 Research Team Develops Physics-Based AI Analysis Technology
- Prof. Seung-Hoon Lee鈥檚 Team from the Department of Physics Presents a Physics-Informed Strategy for Maximizing Learning Efficiency
Professor Lee Seung-hoon鈥檚 research team from the Department of Physics at 狠狠撸 (President Bae Sang-hoon) has developed a machine learning-based technology capable of analyzing the properties of superconductors rapidly and accurately within just tens of milliseconds.
Professor Lee Seung-hoon, along with lead author Lee Dong-ik (master鈥檚 program), published their study titled 鈥淩apid analysis of point-contact Andreev reflection spectra via machine learning with physics-guided data augmentation鈥� in <Materials Today Physics> (Impact Factor: 9.7), a prestigious international journal in the field of applied physics.
This study has also been highly regarded academically, as it proposes a strategy to maximize model learning efficiency based on a solid understanding of physics.
Superconductors are materials that exhibit zero electrical resistance, making them essential for various applications such as lossless power transmission, high-field medical equipment (e.g., MRI), and as core materials for quantum computers. With the recent spotlight on high-temperature superconductors following the LK-99 controversy and the growing interest in next-generation quantum computers based on topological superconductors, the need for technology that can quickly and accurately distinguish between various types of superconductors has become increasingly important.
Professor Lee Seung-hoon鈥檚 research team adopted machine learning technology to significantly improve the accuracy and reduce the analysis time of point-contact spectroscopy (PCS), a technique used for analyzing superconductors. While traditional spectrum analysis could take anywhere from several hours to days, the newly developed model enables highly accurate analysis in under 0.1 seconds.
Professor Lee Seung-hoon explained, 鈥淭raining an AI model is similar to teaching a baby what a pig is. By repeatedly showing images that emphasize defining features鈥�like a pig鈥檚 snout鈥�and saying, 鈥楾his is a pig,鈥� the baby naturally learns that the snout is a key clue in identifying a pig鈥� (see Figure 1).
Professor Lee Seung-hoon鈥檚 research team designed a model that generates large volumes of theoretical spectra for training and incorporated distorted data emphasizing key spectral features鈥�based on physics knowledge鈥�to enhance the model鈥檚 learning. This strategy maximized learning efficiency and significantly improved real-world performance, including analysis accuracy (see Figure 2).
Professor Lee Seung-hoon stated, 鈥淭his research is significant not only because it drastically reduced analysis time, but because it presents a physics-guided strategy to maximize machine learning efficiency.鈥� He added, 鈥淭his technology is expected to accelerate new superconductor research and be broadly applicable to data analysis in fields such as materials science, biomedical engineering, and sensor technology.鈥�
(https://doi.org/10.1016/j.mtphys.2025.101792)