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Rewriting the rules of genetics: Study reveals gene boundaries are dynamic, not fixed

Molecular biologists have long believed that the beginning of a gene launched the process of transcription—the process by which a segment of DNA is copied into RNA and then RNA helps make the proteins that cells need to function.

But a new study published in Science by researchers at Boston University and the University of Massachusetts T.H. Chan School of Medicine challenges that understanding, revealing that the beginning and end of genes are not fixed points, but move together—reshaping how cells build proteins and adapt through evolution.

“This work rewrites a textbook idea: the beginning of a gene doesn’t just launch transcription—it helps decide where it stops and what protein you ultimately make,” says Ana Fiszbein, assistant professor of biology and faculty fellow of computing & data sciences, and one of the lead authors of the study.

Physics-informed AI excels at large-scale discovery of new materials

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.

The playbook for perfect polaritons: Rules for creating quasiparticles that can power optical computers, quantum devices

Light is fast, but travels in long wavelengths and interacts weakly with itself. The particles that make up matter are tiny and interact strongly with each other, but move slowly. Together, the two can combine into a hybrid quasiparticle called a polariton that is part light, part matter.

In a new paper published today in Chem, a team of Columbia chemists has identified how to combine matter and light to get the best of both worlds: polaritons with and fast, wavelike flow. These distinctive behaviors can be used to power and other light-based quantum devices.

“We’ve written a playbook for the ‘perfect’ that will guide our research, and we hope, that of the entire field working on strong light-matter interactions,” said Milan Delor, associate professor of chemistry at Columbia.

Stable ferroaxial states offer a new type of light-controlled non-volatile memory

Ferroic materials such as ferromagnets and ferroelectrics underpin modern data storage, yet face limits: They switch slowly, or suffer from unstable polarization due to depolarizing fields respectively. A new class, ferroaxials, avoids these issues by hosting vortices of dipoles with clockwise or anticlockwise textures, but are hard to control.

Researchers at the Max-Planck-Institute for the Structure and Dynamics of Matter (MPSD) and the University of Oxford now show that bi-stable ferroaxial states can be switched with single flashes of polarized terahertz light. This enables ultrafast, light-controlled and stable switching, a platform for next-generation non-volatile data storage. The work is published in the journal Science.

Modern society relies on , where all information is fundamentally encoded in a of 0s and 1s. Consequently, any physical system capable of reliably switching between two stable states can, in principle, serve as a medium for digital data storage.

Freely levitating rotor spins out ultraprecise sensors for classical and quantum physics

With a clever design, researchers have solved eddy-current damping in macroscopic levitating systems, paving the way for a wide range of sensing technologies.

Levitation has long been pursued by stage magicians and physicists alike. For audiences, the sight of objects floating midair is wondrous. For scientists, it’s a powerful way of isolating objects from external disturbances.

This is particularly useful in the case of rotors, as their torque and , used to measure gravity, gas pressure, momentum, among other phenomena in both classical and , can be strongly influenced by friction. Freely suspending the rotor could drastically reduce these disturbances, and now, researchers from the Okinawa Institute of Science and Technology (OIST) have designed, created, and analyzed such a macroscopic device, bringing the magic of near-frictionless levitation down to Earth through precision engineering.

Controlling atomic interactions in ultracold gas ‘at the push of a button’

Changing interactions between the smallest particles at the touch of a button: Quantum researchers at RPTU have developed a new tool that makes this possible. The new approach—a temporally oscillating magnetic field—has the potential to significantly expand fundamental knowledge in the field of quantum physics. It also opens completely new perspectives on the development of new materials.

Computer chips, imaging techniques such as imaging, , transistors, and : many milestones in our modern everyday world would not have been possible without the discoveries of quantum physics. What is remarkable is that it was only about a hundred years ago that physicists discovered that the world at the smallest scales cannot be explained by the laws of classical physics.

Atoms and their components, protons, neutrons, and electrons—but also light particles—sometimes exhibit physical behaviors that are unknown in the macroscopic world. To this day, the quantum world therefore holds unclear and surprising phenomena that—once understood and controllable—could revolutionize future technologies.

A new method to build more energy-efficient memory devices could lead to a sustainable data future

A research team led by Kyushu University has developed a new fabrication method for energy-efficient magnetic random-access memory (MRAM) using a new material called thulium iron garnet (TmIG) that has been attracting global attention for its ability to enable high-speed, low-power information rewriting at room temperature. The team hopes their findings will lead to significant improvements in the speed and power efficiency of high-computing hardware, such as that used to power generative AI.

The work is published in npj Spintronics.

The rapid spread of generative AI has made the power demand from data centers a global issue, creating an urgent need to improve the energy efficiency of the hardware that runs the technology.

California physicist and Nobel laureate John Martinis won’t quit on quantum computers

A California physicist and Nobel laureate who laid the foundation for quantum computing isn’t done working.

For the last 40 years, John Martinis has worked—mostly within California—to create the fastest computers ever built.

“It’s kind of my professional dream to do this by the time I’m really too old to retire. I should retire now, but I’m not doing that,” the now 67-year-old said.

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