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Category: computing
Researchers visualize crystal phase changes particle by particle in new simulations
The secret to how steel hardens and shape-memory alloys snap into place lies in rapid, atomic-scale shifts that scientists have struggled to observe in materials. Now, Cornell researchers are revealing how these transformations unfold, particle by particle, through advanced modeling techniques.
Using custom-built computer simulations, Julia Dshemuchadse, assistant professor of materials science and engineering at Cornell Engineering, and Hillary Pan, Ph.D., have visualized solid-solid phase transitions in unprecedented detail, capturing the motion of every particle in a theoretical material as its crystal structure morphs into another.
Their findings, published in the Proceedings of the National Academy of Sciences, reveal not only classical transformation mechanisms, but also entirely new ones, reshaping how scientists understand this fundamental process in materials science.
Researchers demonstrate modular approach for building scalable quantum computers
What do children’s building blocks and quantum computing have in common? The answer is modularity.
It is difficult for scientists to build quantum computers monolithically—that is, as a single large unit. Quantum computing relies on the manipulation of millions of information units called qubits, but these qubits are difficult to assemble. The solution? Finding modular ways to construct quantum computers. Like plastic children’s bricks that lock together to create larger, more intricate structures, scientists can build smaller, higher-quality modules and string them together to form a comprehensive system.
Recognizing the potential of these modular systems, researchers from The Grainger College of Engineering at the University of Illinois Urbana-Champaign have presented an enhanced approach to scalable quantum computing by demonstrating a viable and high-performance modular architecture for superconducting quantum processors.
New research fuels the future of data storage: Predicting spin accumulation for faster, greener memory
Researchers from SANKEN (The Institute of Scientific and Industrial Research) at The University of Osaka have developed a new program, “postw90-spin,” that enables high-precision calculations of a novel performance indicator for the spin Hall effect, a phenomenon crucial for developing energy-efficient and high-speed next-generation magnetic memory devices.
This breakthrough addresses a long-standing challenge in spintronics research by providing a definitive measure of the spin Hall effect, overcoming ambiguities associated with traditional metrics. The research is published in the journal npj Spintronics.
The spin Hall effect, where many researchers recognize an electric field generates a perpendicular spin current, is key to spintronic devices. Previously, the spin Hall conductivity was used as a performance indicator. However, this metric is affected by how the spin current is defined, leading to inconsistencies.
New reconfigurable memristor-based system enables in-memory data sorting
Organizing data in a specific order, also known as sorting, is a central computing operation performed by a wide range of systems. Conventional hardware systems rely on separate components to store and sort data, which limits their speed and energy efficiency.
Researchers at Peking University have recently developed a new reconfigurable sort-in-memory system that relies on memristors to in-situ sort stored data. Their proposed system, outlined in a paper published in Nature Electronics and led by Professor Yuchao Yang, was found to store and sort data both quickly and energy-efficiently.
“The original idea comes from the fact that although operations like matrix multiplication and convolution have been widely implemented in CIM (Computing-in-Memory) systems, sorting has long been regarded as a ‘hard nut to crack’ in computing-in-memory technology due to its unique computational characteristics,” Yaoyu Tao, corresponding author of the paper, told TechXplore.
Gold clusters mimic atomic spin properties for scalable quantum computing applications
The efficiency of quantum computers, sensors and other applications often relies on the properties of electrons, including how they are spinning. One of the most accurate systems for high-performance quantum applications relies on tapping into the spin properties of electrons of atoms trapped in a gas, but these systems are difficult to scale up for use in larger quantum devices like quantum computers.
Now, a team of researchers from Penn State and Colorado State has demonstrated how a gold cluster can mimic these gaseous, trapped atoms, allowing scientists to take advantage of these spin properties in a system that can be easily scaled up.
“For the first time, we show that gold nanoclusters have the same key spin properties as the current state-of-the-art methods for quantum information systems,” said Ken Knappenberger, department head and professor of chemistry in the Penn State Eberly College of Science and leader of the research team.
From cosmic strings to computer chips: Cooling rate triggers phase transitions in silicon surfaces
Solar cells and computer chips need silicon layers that are as perfect as possible. Every imperfection in the crystalline structure increases the risk of reduced efficiency or defective switching processes.
If you know how silicon atoms arrange themselves to form a crystal lattice on a thin surface, you gain fundamental insights into controlling crystal growth. To this end, an international research team analyzed the behavior of silicon that was flash-frozen. The study is published in the journal Physical Review Letters.
The results show that the speed of cooling has a major impact on the structure of silicon surfaces. The underlying mechanism may also have occurred during phase transitions in the early universe shortly after the Big Bang.