Engineers and scientists, as well as artists, have long been inspired by the beauty and functionality of nature’s designs. Japan designed high-speed trains to cut through the air as smoothly as the kingfisher cuts through water, for example, but useful designs can also be found at a microscopic level. The study of biology in combination with materials science is called biomateriomics. An Italian research team sees great potential in the application of generative artificial intelligence to this already interdisciplinary field. They have described this potential, and the associated limitations and challenges, in an open access review article titled “Generative Artificial Intelligence for Advancing Discovery and Design in Biomateriomics,” published May 1 in Intelligent Computing, a Science Partner Journal.

An international team of researchers has reviewed the latest advances in multimodal artificial intelligence (AI) for cardiovascular diseases (CVD), highlighting its superior diagnostic accuracy, risk prediction, and therapeutic guidance compared with traditional single-data approaches. The review outlines how integrating imaging, genomics, electronic health records, and wearable data into unified AI models can enable earlier diagnosis, personalized therapy, and continuous remote monitoring, heralding a new era of precision cardiology.

With the rapid spread of generative AI, the demand for more energy-efficient methods of powering the hardware is becoming apparent. Now, researchers have succeeded in applying on-axis magnetron sputtering on thulium iron garnet (TmIG)—a promising material that can enable high-speed, low-power information rewriting at room temperature—to build more energy-efficient magnetic random-access memory.

Tilted metasurface nanostructures, with excellent physical properties and enormous application potential, pose an urgent need for manufacturing methods. Here, electric-field-driven generative-nanoimprinting technique is proposed. The electric field applied between the template and the substrate drives the contact, tilting, filling, and holding processes. By accurately controlling the introduced included angle between the flexible template and the substrate, tilted nanostructures with a controllable angle are imprinted onto the substrate, although they are vertical on the template. By flexibly adjusting the electric field intensity and the included angle, large-area uniform-tilted, gradient-tilted, and high-angle-tilted nanostructures are fabricated. In contrast to traditional replication, the morphology of the nanoimprinting structure is extended to customized control. This work provides a cost-effective, efficient, and versatile technology for the fabrication of various large-area tilted metasurface structures. As an illustration, a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays, demonstrating superior imaging quality.

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.