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.

KAIST (President Kwang Hyung Lee) announced on the 2nd of October that 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 (President Jinsang Kim) and Dr. Byungki Ryu at the Korea Electrotechnology Research Institute (President Namkyun Kim), 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.

Recent years have witnessed transformative changes brought about by artificial intelligence (AI) techniques with billions of parameters for the realization of high accuracy, proposing high demand for the advanced and AI chip to solve these AI tasks efficiently and powerfully. Rapid progress has been made in the field of advanced chips recently, such as the development of photonic computing, the advancement of the quantum processors, the boost of the biomimetic chips, and so on. Designs tactics of the advanced chips can be conducted with elaborated consideration of materials, algorithms, models, architectures, and so on. Though a few reviews present the development of the chips from their unique aspects, reviews in the view of the latest design for advanced and AI chips are few. Here, the newest development is systematically reviewed in the field of advanced chips. First, background and mechanisms are summarized, and subsequently most important considerations for co-design of the software and hardware are illustrated. Next, strategies are summed up to obtain advanced and AI chips with high excellent performance by taking the important information processing steps into consideration, after which the design thought for the advanced chips in the future is proposed. Finally, some perspectives are put forward.

Recently, the iGaN Laboratory led by Professor Haiding Sun at the School of Microelectronics, University of Science and Technology of China (USTC) of Chinese Academy of Sciences(CAS), together with a global collaborative team from Wuhan University, Zhejiang University and University of Cambridge, has successfully developed the first miniaturized ultraviolet (UV) spectrometer and realized on-chip spectral imaging. Based on a novel gallium nitride (GaN) cascaded photodiode architecture and integrated with deep neural network (DNN) algorithms, the device achieves high-precision spectral detection and high-resolution multispectral imaging. With a response speed on the nanosecond scale, it sets a new world record for the fastest reported miniaturized spectrometer. The work was published online in Nature Photonics on September 26, 2025.

Researchers at University of Tsukuba have developed DeMemSeg, an AI-powered analysis pipeline that addresses a long-standing challenge in microscopy: precisely segmenting and measuring individual cell membranes that overlap in two-dimensional (2D) projection images. This innovation is expected to accelerate research on cellular mechanisms and related diseases.

For the nearly 3 million patients with idiopathic pulmonary fibrosis (IPF), what typically starts as shortness of  breath can rapidly worsen into a suffocating inability to breathe, often leading to a dangerous elevation in pulmonary artery pressure, related heart attacks or fatal respiratory failure within three to five years of diagnosis. USC physician-scientists Denis Evseenko and Toby Maher hope to offer these patients a better prognosis by developing a regenerative drug to block the cells that promote fibrosis, or scarring, in the lungs, with support from a five-year $3.2 million grant from the National Institute on Aging, part of the National Institutes of Health (NIH). Evseenko has already developed two drug-like molecules designed to stop immune cells and small blood vessels from initiating the cellular stress response that triggers inflammation and fibrosis. By blocking inflammation and fibrosis, these drug-like molecules promote a healthier environment conducive to regeneration. To develop these drug candidates for IPF, Evseenko is teaming up with Maher, who is a professor of clinical medicine and director of interstitial lung Disease at the Keck School of Medicine. An experienced pulmonologist recently recruited to the USC Hastings Center for Pulmonary Research, Maher has a biorepository of patient lung specimens and has been involved in more than 100 clinical trials for IPF and other fibrotic lung diseases. Through their collaboration, the team will test the safety and therapeutic potential of the two drug-like molecules in animals and human cells, and develop the most effective one into an oral pill—ideally ready for human clinical trials by the time the grant concludes in five years.