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Theoretical particle physicist tackles machine learning’s black box

From self-driving cars to facial recognition, modern life is growing more dependent on machine learning, a type of artificial intelligence (AI) that learns from datasets without explicit programming.

Despite its omnipresence in society, we’re just beginning to understand the mechanisms driving the technology. In a recent study, Zhengkang (Kevin) Zhang, assistant professor in the University of Utah’s Department of Physics & Astronomy, demonstrated how physicists can play an important role in unraveling its mysteries.

“People used to say is a black box—you input a lot of data and at some point, it reasons and speaks and makes decisions like humans do. It feels like magic because we don’t really know how it works,” said Zhang. “Now that we’re using AI across many critical sectors of society, we have to understand what our machine learning models are really doing—why something works or why something doesn’t work.”

Quasi-solid electrolyte developed for safer and greener lithium-ion batteries

3D-SLISE is a quasi-solid electrolyte developed at the Institute of Science Tokyo, which enables safe, fast-charging/discharging of 2.35 V lithium-ion batteries to be fabricated under ambient conditions. With energy-efficient manufacturing using raw materials free from flammable organic solvents, the technique eliminates the need for dry rooms or high-temperature processing. Moreover, it also allows direct recovery of active materials through water dispersal—ensuring a sustainable, recyclable approach to battery production.

In today’s era of portable power and , form the backbone of modern technology—powering everything from smartphones to electric vehicles. While demand for lithium-ion batteries continues to grow, so do concerns about their safety, environmental impact, and recyclability. Most lithium-ion batteries that rely on flammable organic solvents are energy-intensive to manufacture, and require complicated recycling processes. These issues not only drive up costs but also pose serious safety and —highlighting the need for safer and cleaner alternatives.

To address this challenge, a research team from Institute of Science Tokyo (Science Tokyo), Japan, led by Specially Appointed Professor Yosuke Shiratori and Associate Professor Shintaro Yasui from the Zero-Carbon Energy Research Institute, Science Tokyo, developed a new quasi-solid electrolyte called 3D-Slime Interface Quasi-Solid Electrolyte (3D-SLISE), which can transform battery manufacturing. With a simple borate-water matrix, the electrolyte supports the production of 2.35 V lithium-ion batteries under standard air conditions. The detailed findings of the study were made available in the journal Advanced Materials on July 9, 2025.

Microscopic imaging reveals how electric double layers form at battery nucleation sites

Electrochemical cells—or batteries, as a well-known example—are complex technologies that combine chemistry, physics, materials science and electronics. More than power sources for everything from smartphones to electric vehicles, they remain a strong motivation for scientific inquiry that seeks to fully understand their structure and evolution at the molecular level.

A team led by Yingjie Zhang, a professor of and engineering in The Grainger College of Engineering at the University of Illinois Urbana-Champaign, has completed the first investigation into a widely acknowledged but often overlooked aspect of : the nonuniformity of the liquid at the solid-liquid interfaces in the cells.

As the researchers report in the Proceedings of the National Academy of Sciences, microscopic imaging revealed that these interfacial structures, called electrical double layers (EDLs), tend to organize into specific configurations in response to chemical deposition on the of the solid. The paper is titled “Nucleation at solid–liquid interfaces is accompanied by the reconfiguration of electrical double layers.”

Tesla’s New Strategy Has Uber Terrified

Questions to inspire discussion.

👥 Q: What is the ratio of robo taxis to supervisors in Tesla’s network? A: Tesla’s robo taxi network operates with a 10:1 ratio of robo taxis to supervisors, enabling efficient management and cost-effective operations.

Market Disruption.

📊 Q: How is Whim, a Tesla competitor, performing in the market? A: As of April 2025, Whim has 25% of San Francisco gross bookings, surpassing Lyft, with an average price of $20 per mile compared to Uber’s $15 and Lyft’s $14.

Technology Superiority.

🤖 Q: How does Tesla’s robo taxi software compare to human drivers? A: Tesla’s robo taxi software has crossed the uncanny valley, providing a smooth and comfortable driving experience similar to a human chauffeur, outperforming Uber’s inconsistent service.

Continue reading “Tesla’s New Strategy Has Uber Terrified” | >

Artificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computing

A spiking neural network consists of artificial synapses and neurons and may realize human-level intelligence. Unlike the widely reported artificial synapses, the fabrication of large-scale artificial neurons with good performance is still challenging due to the lack of a suitable material system and integration method. Here, we report an ultrathin (less than10 nm) and inch-size two-dimensional (2D) oxide-based artificial neuron system produced by a controllable assembly of solution-processed 2D monolayer TiOx nanosheets. Artificial neuron devices based on such 2D TiOx films show a high on/off ratio of 109 and a volatile resistance switching phenomenon. The devices can not only emulate the leaky integrate-and-fire activity but also self-recover without additional circuits for sensing and reset. Moreover, the artificial neuron arrays are fabricated and exhibited good uniformity, indicating their large-area integration potential. Our results offer a strategy for fabricating large-scale and ultrathin 2D material-based artificial neurons and 2D spiking neural networks.

Things Tesla won’t tell you about Robotaxi (Highlights)

Questions to inspire discussion.

🛑 Q: How does the Robo Taxi handle blocked routes? A: The Robo Taxi demonstrates impressive rerouting capabilities, finding new paths when exits are blocked and making right-hand turns to circumvent blocked left-hand turn lanes.

🚦 Q: How does the Robo Taxi adapt to traffic situations? A: It shows human-like behavior by slowing down dramatically to enter the right-hand lane when a slower vehicle is ahead, and can accelerate and speed up to overtake slower vehicles.

💧 Q: How does the Robo Taxi handle standing water? A: The Robo Taxi demonstrates adaptability by avoiding standing water in parking lots, performing three-point turns to navigate around obstacles.

🔄 Q: How flexible is the Robo Taxi in changing its driving approach? A: It shows impressive adaptability by altering its method to slow down when encountering slower vehicles and changing again to make right-hand turns around blocked left-hand turn lanes.

Technical Considerations.

Continue reading “Things Tesla won’t tell you about Robotaxi (Highlights)” | >

New physical model aims to boost energy storage research

Engineers rely on computational tools to develop new energy storage technologies, which are critical for capitalizing on sustainable energy sources and powering electric vehicles and other devices. Researchers have now developed a new classical physics model that captures one of the most complex aspects of energy storage research—the dynamic nonequilibrium processes that throw chemical, mechanical and physical aspects of energy storage materials out of balance when they are charging or discharging energy.

The new Chen-Huang Nonequilibrium Phasex Transformation (NExT) Model was developed by Hongjiang Chen, a former Ph.D. student at NC State, in conjunction with his advisor, Hsiao-Ying Shadow Huang, who is an associate professor of mechanical and aerospace engineering at the university. A paper on the work, “Energy Change Pathways in Electrodes during Nonequilibrium Processes,” is published in The Journal of Physical Chemistry C.

But what are “nonequilibrium processes”? Why are they important? And why would you want to translate those processes into mathematical formulae? We talked with Huang to learn more.

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