Imagine a future where our devices are not just faster and more powerful, but also incredibly energy-efficient. Sounds like a dream, right? But here’s the catch: to make this a reality, we need to crack the code on how tiny devices use energy today—a task far more complex than it seems. Memory storage, information processing, and energy consumption in these technologies involve a constant, chaotic flow of energy, never settling into a stable state. And to truly understand this, scientists are diving into the quantum realm, where the rules of the game are entirely different.
A groundbreaking study from Stanford, published on February 9 in Nature Physics (https://www.nature.com/articles/s41567-026-03177-8), combines theory, experimentation, and machine learning to measure energy costs in non-equilibrium processes with unprecedented precision. The researchers focused on quantum dots, tiny nanocrystals with unique light-emitting properties that emerge from quantum effects at the nanoscale. By measuring the entropy production of these dots—a metric that reveals how reversible a process is and encodes data about memory, information loss, and energy costs—they’ve unlocked a new way to determine the ultimate speed and efficiency limits of devices.
But here’s where it gets controversial: Grant Rotskoff, assistant professor of chemistry and co-author of the paper, admits, ‘When I first saw this work, they really had to convince me that they were measuring what they claimed, because it’s an incredibly hard thing to do.’ (https://profiles.stanford.edu/rotskoff) This skepticism highlights just how cutting-edge—and debated—this research is.
Many materials and devices shift between structural phases at the atomic level, involving ultrafast motions. By improving measurements of the interplay between memory, information, and energy dissipation, this research could redefine what’s possible for computers and similar devices in terms of energy use, efficiency, stability, and speed. And this is the part most people miss: the world around us—from weather patterns to living organisms—is inherently non-equilibrium, driven by processes that are notoriously difficult to measure. ‘No one has ever been able to measure things like entropy production in real material systems before. That’s what our paper achieves,’ said Aaron Lindenberg, the study’s senior author and professor of materials science and engineering. (https://profiles.stanford.edu/aaron-lindenberg)
Starting with a complex, nanoscale system, the researchers hope to lay the groundwork for devices across scales to evolve in ways that consume less energy and operate faster. But is this approach too idealistic? Lead author Yuejun Shen, a graduate student in the Lindenberg lab, acknowledges the challenges: ‘There’s a lot of theory in this area, but experiments often fall short because the parameters are too idealized or there’s too much noise. Our work bridges that gap.’ (https://profiles.stanford.edu/yuejun-shen)
Measuring efficiency at the nanoscale is no small feat. While classical thermodynamics gives us tools to measure efficiency in systems like engines, those tools fail at the nanoscale. ‘There’s a huge gap between what we can do theoretically and what’s possible experimentally,’ Rotskoff explains. ‘This work is a significant step toward closing that gap.’ For instance, by manipulating external fields, the researchers induced non-equilibrium states in quantum dots, causing them to blink in distinct statistical patterns—a key to understanding information dissipation.
After collecting experimental data, the team used machine learning to optimize a physics-based model, enabling them to calculate entropy production for the quantum dots. This fusion of cutting-edge computation, measurement, and theory opens up new possibilities for innovation. Just a decade ago, the computer vision techniques, machine learning algorithms, and computing power required for this work would have been unimaginable. ‘Conceptually, I’m not sure the question could have been formulated as clearly 10 years ago,’ Rotskoff reflects.
The researchers believe their technique can become even more precise, drawing on rapid advancements in multiple fields. But here’s a thought-provoking question: Can this approach truly revolutionize device efficiency, or are we still too far from practical applications? Lindenberg is optimistic: ‘If we can directly measure energy dissipation in non-equilibrium systems, we can explore pathways to optimize processes—like finding devices that use less energy or operate faster. It’s a problem of immense technological importance.’
As we stand on the brink of this scientific breakthrough, one thing is clear: the future of energy-efficient devices may hinge on our ability to measure the unmeasurable. What do you think? Is this the key to unlocking a more sustainable technological future, or are there hurdles we’re not yet considering? Share your thoughts in the comments below!
