Toggle light / dark theme

Machine learning unravels quantum atomic vibrations in materials

Caltech scientists have developed an artificial intelligence (AI)–based method that dramatically speeds up calculations of the quantum interactions that take place in materials. In new work, the group focuses on interactions among atomic vibrations, or phonons—interactions that govern a wide range of material properties, including heat transport, thermal expansion, and phase transitions. The new machine learning approach could be extended to compute all quantum interactions, potentially enabling encyclopedic knowledge about how particles and excitations behave in materials.

Scientists like Marco Bernardi, professor of applied physics, physics, and at Caltech, and his graduate student Yao Luo (MS ‘24) have been trying to find ways to speed up the gargantuan calculations required to understand such particle interactions from first principles in real materials—that is, beginning with only a material’s atomic structure and the laws of quantum mechanics.

Last year, Bernardi and Luo developed a data-driven method based on a technique called singular value decomposition (SVD) to simplify the enormous mathematical matrices scientists use to represent the interactions between electrons and phonons in a material.

Systematic fraud uncovered in mathematics publications

An international team of authors led by Ilka Agricola, professor of mathematics at the University of Marburg, Germany, has investigated fraudulent practices in the publication of research results in mathematics on behalf of the German Mathematical Society (DMV) and the International Mathematical Union (IMU), documenting systematic fraud over many years.

The results of the study were recently posted on the arXiv preprint server and in the Notices of the American Mathematical Society and have since caused a stir among mathematicians.

To solve the problem, the study also provides recommendations for the publication of research results in mathematics.

Tesla AI5 & AI6 Chips “Compressing Reality”?! What Did Elon See?!

Elon Musk has revealed Tesla’s new AI chips, AI5 and AI6, which will drive the company’s shift towards AI-powered services, enabling significant advancements in Full Self-Driving capabilities and potentially revolutionizing the self-driving car industry and beyond.

## Questions to inspire discussion.

Tesla’s AI Chip Advancements.

🚀 Q: What are the key features of Tesla’s AI5 and AI6 chips? A: Tesla’s AI5 and AI6 chips are inference-first, designed for high-throughput and efficient processing of AI models on devices like autos, Optimus, and Grok voice agents, being 40x faster than previous models.

💻 Q: How do Tesla’s AI5 and AI6 chips compare to previous models? A: Tesla’s AI5 chip is a 40x improvement over AI4, with 500 TOPS expanding to 5,000 TOPS, enabling excellent performance in full self-driving and Optimus humanoid robots.

🧠 Q: What is the significance of softmax in Tesla’s AI5 chip? A: AI5 is designed to run softmax natively in a few steps, unlike AI4 which relies on CPU and runs softmax in 40 steps in emulation mode.

Mathematical model of memory suggests seven senses are optimal

Skoltech scientists have devised a mathematical model of memory. By analyzing its new model, the team came to surprising conclusions that could prove useful for robot design, artificial intelligence, and for better understanding of human memory. Published in Scientific Reports, the study suggests there may be an optimal number of senses—if so, those of us with five senses could use a couple more.

“Our conclusion is, of course, highly speculative in application to human senses, although you never know: It could be that humans of the future would evolve a sense of radiation or magnetic field. But in any case, our findings may be of practical importance for robotics and the theory of ,” said study co-author Professor Nikolay Brilliantov of Skoltech AI.

“It appears that when each retained in memory is characterized in terms of seven features—as opposed to, say, five or eight—the of distinct objects held in memory is maximized.”

Mathematical ‘sum of zeros’ trick exposes topological magnetization in quantum materials

A new study addresses a foundational problem in the theory of driven quantum matter by extending the Středa formula to non-equilibrium regimes. It demonstrates that a superficially trivial “sum of zeros” encodes a universal, quantized magnetic response—one that is intrinsically topological and uniquely emergent under non-equilibrium driving conditions.

Imagine a strange material being rhythmically pushed—tapped again and again by invisible hands. These are periodically driven , or Floquet systems, where energy is no longer conserved in the usual sense. Instead, physicists speak of quasienergy—a looping spectrum with no clear start or end.

When scientists measure how such a system responds to a magnetic field, every single contribution seems to vanish—like adding an infinite list of zeros. And yet, the total stubbornly comes out finite, quantized, and very real.

Clocks created from random events can probe ‘quantumness’ of universe

A newly discovered set of mathematical equations describes how to turn any sequence of random events into a clock, scientists at King’s College London reveal. The paper is published in the journal Physical Review X.

The researchers suggest that these formulas could help to understand how cells in our bodies measure time and to detect the effects of quantum mechanics in the wider world.

Studying these timekeeping processes could have far-reaching implications, helping us to understand proteins with rhythmic movements which malfunction in motor neuron disease or chemical receptors that cells use to detect harmful toxins.

What Is Superposition and Why Is It Important?

Imagine touching the surface of a pond at two different points at the same time. Waves would spread outward from each point, eventually overlapping to form a more complex pattern. This is a superposition of waves. Similarly, in quantum science, objects such as electrons and photons have wavelike properties that can combine and become what is called superposed.

While waves on the surface of a pond are formed by the movement of water, quantum waves are mathematical. They are expressed as equations that describe the probabilities of an object existing in a given state or having a particular property. The equations might provide information on the probability of an electron moving at a specific speed or residing in a certain location. When an electron is in superposition, its different states can be thought of as separate outcomes, each with a particular probability of being observed. An electron might be said to be in a superposition of two different velocities or in two places at once. Understanding superposition may help to advance quantum technology such as quantum computers.


One of the fundamental principles of quantum mechanics, superposition explains how a quantum state can be represented as the sum of two or more states.

Physicists demonstrate controlled expansion of quantum wavepacket in a levitated nanoparticle

Quantum mechanics theory predicts that, in addition to exhibiting particle-like behavior, particles of all sizes can also have wave-like properties. These properties can be represented using the wave function, a mathematical description of quantum systems that delineates a particle’s movements and the probability that it is in a specific position.

Fat microscopy to image lipids in cells

Lipid molecules, or fats, are crucial to all forms of life. Cells need lipids to build membranes, separate and organize biochemical reactions, store energy, and transmit information. Every cell can create thousands of different lipids, and when they are out of balance, metabolic and neurodegenerative diseases can arise. It is still not well understood how cells sort different types of lipids between cell organelles to maintain the composition of each membrane. A major reason is that lipids are difficult to study, since microscopy techniques to precisely trace their location inside cells have so far been missing.

The researchers developed a method that enables visualizing lipids in cells using standard fluorescence microscopy. After the first successful proof of concept, the authors brought mass-spectrometry expert on board to study how lipids are transported between cellular organelles.

“We started our project with synthesizing a set of minimally modified lipids that represent the main lipids present in organelle membranes. These modified lipids are essentially the same as their native counterparts, with just a few different atoms that allowed us to track them under the microscope,” explains a PhD student in the group.

The modified lipids mimic natural lipids and are “bifunctional,” which means they can be activated by UV light, causing the lipid to bind or crosslink with nearby proteins. The modified lipids were loaded in the membrane of living cells and, over time, transported into the membranes of organelles. The researchers worked with human cells in cell culture, such as bone or intestinal cells, as they are ideal for imaging.

“After the treatment with UV light, we were able to monitor the lipids with fluorescence microscopy and capture their location over time. This gave us a comprehensive picture of lipid exchange between cell membrane and organelle membranes,” concludes the author.

In order to understand the microscopy data, the team needed a custom image analysis pipeline. “To address our specific needs, I developed an image analysis pipeline with automated image segmentation assisted by artificial intelligence to quantify the lipid flow through the cellular organelle system,” says another author.

By combining the image analysis with mathematical modeling, the research team discovered that between 85% and 95% of the lipid transport between the membranes of cell organelles is organized by carrier proteins that move the lipids, rather than by vesicles. This non-vesicular transport is much more specific with regard to individual lipid species and their sorting to the different organelles in the cell. The researchers also found that the lipid transport by proteins is ten times faster than by vesicles. These results imply that the lipid compositions of organelle membranes are primarily maintained through fast, species-specific, non-vesicular lipid transport.

/* */