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Category: robotics/AI – Page 2

AI tools identify promising alternatives to lithium-ion batteries for energy storage

Researchers from New Jersey Institute of Technology (NJIT) have used artificial intelligence to tackle a critical problem facing the future of energy storage: finding affordable, sustainable alternatives to lithium-ion batteries.

In research published in Cell Reports Physical Science, the NJIT team led by Professor Dibakar Datta successfully applied generative AI techniques to rapidly discover new porous materials capable of revolutionizing multivalent-ion batteries. These batteries, using abundant elements like magnesium, calcium, aluminum and zinc, offer a promising, cost-effective alternative to , which face global supply challenges and sustainability issues.

Unlike traditional lithium-ion batteries, which rely on lithium ions that carry just a single positive charge, multivalent-ion batteries use elements whose ions carry two or even three positive charges. This means multivalent-ion batteries can potentially store significantly more energy, making them highly attractive for future energy storage solutions.

A guidance to intelligent metamaterials and metamaterials intelligence

The bidirectional interactions between metamaterials and artificial intelligence have recently attracted much attention. Here, the authors stand from a unified perspective to discuss intelligent metamaterials (AI for metamaterials) and metamaterials intelligence (metamaterials for AI).

Metamaterials: Highly Twisted Rods Store Large Amounts of Energy

An international research team coordinated at KIT (Karlsruhe Institute of Technology) has developed mechanical metamaterials with a high elastic energy density. Highly twisted rods that deform helically provide these metamaterials with a high stiffness and enable them to absorb and release large amounts of elastic energy. The researchers conducted simple compression experiments to confirm the initial theoretical results. Their findings have been published in the science journal Nature. (DOI: 10.1038/s41586-025–08658-z)

Be it springs for absorbing energy, buffers for mechanical energy storage, or flexible structures in robotics or energy-efficient machines: Storage of mechanical energy is required for many technologies. Kinetic energy, i.e. motion energy or the corresponding mechanical work, is converted into elastic energy in such a way that it can be fully released again when required. The key characteristic here is enthalpy – the energy density that can be stored in and recovered from an element of the material. Peter Gumbsch, Professor for mechanics of materials at KIT’s Institute for Applied Materials (IAM), explains that achieving the highest possible enthalpy is challenging: “The difficulty is to combine conflicting properties: high stiffness, high strength and large recoverable strain.”

Clever arrangement of helically deformed rods in metamaterials.

New AI tool learns to read medical images with far less data

A new artificial intelligence (AI) tool could make it much easier—and cheaper—for doctors and researchers to train medical imaging software, even when only a small number of patient scans are available.

The AI tool improves upon a process called medical image , where every pixel in an image is labeled based on what it represents—cancerous or normal tissue, for example. This process is often performed by a highly trained expert, and has shown promise in automating this labor-intensive task.

The big challenge is that deep learning-based methods are data hungry—they require a large amount of pixel-by-pixel annotated images to learn, explained Li Zhang, a Ph.D. student in the Department of Electrical and Computer Engineering at the University of California San Diego. Creating such datasets demands expert labor, time and cost. And for many medical conditions and , that level of data simply doesn’t exist.

Big Tech’s Big Bet on AI Driving $344 Billion in Spend This Year

If there’s any lesson to take from the spending plans issued by the world’s largest technology companies over the past two weeks, it’s to never underestimate the fear of missing out.

Microsoft Corp., which set a $24.2 billion capital spending record last quarter, plans to drop upwards of $30 billion in the current period. Amazon.com Inc. similarly spent $31.4 billion last quarter, almost double what it dropped a year ago, and is maintaining that level of investment. Google owner Alphabet Inc. raised its capital expenditures guidance this year to $85 billion.

Researchers harness AI-powered protein design to enhance T-cell based immunotherapies

A paper published in Cell highlights how researchers have leveraged AI-based computational protein design to create a novel synthetic ligand that activates the Notch signaling pathway, a key driver in T-cell development and function.

These so-called soluble Notch agonists can be broadly applied to optimize clinical T-cell production and advance immunotherapy development.

Notch signaling is central to many cellular differentiation processes and is essential in transforming human immune cells into T-cells that target viruses and tumors. But activating Notch signaling in the laboratory has posed a challenge.

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