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Civil Engineering

“A New Periodic Table of Machine Learning: Unlocking AI Discovery and Innovation”

After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a ‘periodic table of machine learning’ that can help scientists combine elements of different methods to improve algorithms or create new ones.

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MIT researchers have created a groundbreaking periodic table that reveals how more than 20 classical machine-learning algorithms are connected. This innovative framework sheds light on how scientists can fuse strategies from different methods to improve existing AI models or come up with new ones.

The researchers used their framework to combine elements of two different algorithms to create a new image-classification algorithm that performed 8 percent better than current state-of-the-art approaches. This breakthrough demonstrates the potential of the periodic table to unlock AI discovery and innovation.

The periodic table stems from one key idea: All these algorithms learn a specific kind of relationship between data points. While each algorithm may accomplish that in a slightly different way, the core mathematics behind each approach is the same. Building on these insights, the researchers identified a unifying equation that underlies many classical AI algorithms.

They used this equation to reframe popular methods and arrange them into a table, categorizing each based on the approximate relationships it learns. Just like the periodic table of chemical elements, which initially contained blank squares that were later filled in by scientists, the periodic table of machine learning also has empty spaces.

These spaces predict where algorithms should exist, but which haven’t been discovered yet. The researchers filled one gap by borrowing ideas from a machine-learning technique called contrastive learning and applying them to image clustering. This resulted in a new algorithm that could classify unlabeled images 8 percent better than another state-of-the-art approach.

The flexible periodic table allows researchers to add new rows and columns to represent additional types of datapoint connections. Ultimately, having I-Con as a guide could help machine learning scientists think outside the box, encouraging them to combine ideas in ways they wouldn’t necessarily have thought of otherwise.

This research was funded, in part, by the Air Force Artificial Intelligence Accelerator, the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions, and Quanta Computer. The researchers’ work will be presented at the International Conference on Learning Representations.

Civil Engineering

A Groundbreaking Magnetic Trick for Quantum Computing: Stabilizing Qubits with Exotic Materials

Researchers have unveiled a new quantum material that could make quantum computers much more stable by using magnetism to protect delicate qubits from environmental disturbances. Unlike traditional approaches that rely on rare spin-orbit interactions, this method uses magnetic interactions—common in many materials—to create robust topological excitations. Combined with a new computational tool for finding such materials, this breakthrough could pave the way for practical, disturbance-resistant quantum computers.

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A Groundbreaking Magnetic Trick for Quantum Computing: Stabilizing Qubits with Exotic Materials

Quantum computers have long been touted as revolutionaries in solving complex problems that conventional supercomputers can’t handle. However, their development has been hindered by one major challenge: qubits, the basic units of quantum computers, are extremely delicate and prone to losing their quantum states due to external disturbances.

Researchers from Chalmers University of Technology in Sweden and Aalto University and the University of Helsinki in Finland have now made a groundbreaking discovery that could change this. They’ve developed a new type of exotic quantum material that exhibits robust topological excitations, which are significantly more stable and resilient than other quantum states.

This breakthrough is an important step towards realising practical topological quantum computing by constructing stability directly into the material’s design. The researchers’ innovative approach uses magnetism as the key ingredient to achieve this effect, harnessing magnetic interactions to engineer robust topological excitations in a broader spectrum of materials.

“The advantage of our method is that magnetism exists naturally in many materials,” explains Guangze Chen, postdoctoral researcher in applied quantum physics at Chalmers and lead author of the study published in Physical Review Letters. “You can compare it to baking with everyday ingredients rather than using rare spices. This means that we can now search for topological properties in a much broader spectrum of materials, including those that have previously been overlooked.”

To accelerate the discovery of new materials with useful topological properties, the research team has also developed a new computational tool that can directly calculate how strongly a material exhibits topological behavior.

“Our hope is that this approach can help guide the discovery of many more exotic materials,” says Guangze Chen. “Ultimately, this can lead to next-generation quantum computer platforms, built on materials that are naturally resistant to the kind of disturbances that plague current systems.”

This magnetic trick has the potential to revolutionize the development of practical topological quantum computing and pave the way for next-generation quantum computer platforms. As researchers continue to explore and develop new exotic materials with robust topological excitations, we may finally see the dawn of a new era in quantum computing.

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Civil Engineering

AI Breakthrough in Fusion Reactor Design: Uncovering Hidden Safe Zones with HEAT-ML

Scientists have developed a lightning-fast AI tool called HEAT-ML that can spot hidden “safe zones” inside a fusion reactor where parts are protected from blistering plasma heat. Finding these areas, known as magnetic shadows, is key to keeping reactors running safely and moving fusion energy closer to reality.

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The development of artificial intelligence (AI) in fusion research has taken a significant leap forward. A public-private partnership between Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory has led to the creation of HEAT-ML, an AI approach that rapidly finds and simulates “magnetic shadows” in fusion vessels: safe havens protected from intense heat plasma.

HEAT-ML uses a deep neural network to learn how to calculate shadow masks, which are 3D maps of specific areas on internal components shielded from direct heat. This AI surrogate was trained using a database of approximately 1,000 SPARC simulations and can now simulate the same calculations in mere milliseconds, as opposed to the previous 30 minutes.

The goal is to create software that significantly speeds up fusion system design and enables good decision-making during operations by adjusting plasma settings to prevent potential problems. HEAT-ML was specifically designed for a small part of the SPARC tokamak under construction by CFS but has the potential to be expanded to generalize the calculation of shadow masks for exhaust systems of any shape and size, as well as other plasma-facing components.

Researchers believe that this AI breakthrough could pave the way for faster fusion system design, enabling good decision-making during operations, and potentially leading to limitless amounts of electricity on Earth.

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Chemistry

Unlocking the Secrets of Atomic Motion: A Revolutionary Discovery at the Nanoscale

A pioneering team at the University of Maryland has captured the first-ever images of atomic thermal vibrations, unlocking an unseen world of motion within two-dimensional materials. Their innovative electron ptychography technique revealed elusive “moiré phasons,” a long-theorized phenomenon that governs heat, electronic behavior, and structural order at the atomic level. This discovery not only confirms decades-old theories but also provides a new lens for building the future of quantum computing, ultra-efficient electronics, and advanced nanosensors.

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The study of atomic-scale phenomena has led researchers to a groundbreaking discovery that could reshape the design of quantum technologies and ultrathin electronics. Yichao Zhang, an assistant professor in the University of Maryland Department of Materials Science and Engineering, has developed an innovative technique called “electron ptychography” to directly image the thermal vibrations of individual atoms. This achievement was published in the journal Science on July 24.

Two-dimensional materials, which are sheet-like structures a few nanometers thick, have been explored as new components for next-generation quantum and electronic devices. A crucial feature of twisted two-dimensional materials is “moiré phasons,” essential to understanding their thermal conductivity, electronic behavior, and structural order. However, detecting moiré phasons experimentally had proven challenging, hindering further research in these revolutionary materials.

Zhang’s team overcame this challenge by employing electron ptychography, a technique that achieved the highest resolution documented (better than 15 picometers) and detected the blurring of individual atoms caused by thermal vibrations. This groundbreaking study revealed that spatially localized moiré phasons dominate thermal vibrations in twisted two-dimensional materials, fundamentally reshaping our understanding of their impact.

The breakthrough confirmed long-standing theoretical predictions of moiré phasons and demonstrated that electron ptychography can be used to map thermal vibrations with atomic precision for the first time. This achievement opens up new possibilities for exploring previously hidden physics in quantum materials.

“This is like decoding a hidden language of atomic motion,” said Zhang. “Electron ptychography lets us see these subtle vibrations directly. Now we have a powerful new method to explore previously hidden physics, which will accelerate discoveries in two-dimensional quantum materials.”

Zhang’s research team will next focus on resolving how thermal vibrations are affected by defects and interfaces in quantum and electronic materials. Controlling the thermal vibration behavior of these materials could enable the design of novel devices with tailored thermal, electronic, and optical properties – paving the way for advances in quantum computing, energy-efficient electronics, and nanoscale sensors.

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