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Recent studies using advanced supercomputing have focused on the dynamics within copper-based superconductors, aiming to develop materials that are efficient at higher temperatures and could improve electronic devices significantly.

Over the past 35 years, scientists have been studying a remarkable class of materials known as superconductors. When cooled to specific temperatures, these materials allow electricity to flow without any resistance.

A research team utilizing the Summit supercomputer has been delving into the behavior of these superconductors, particularly focusing on how negatively charged particles interact with the smallest units of light within the material. This interaction triggers sudden and dramatic changes in the material’s properties and holds the key to understanding how certain copper-based superconductors function.

The universe just got a whole lot bigger—or at least in the world of computer simulations, that is. In early November, researchers at the Department of Energy’s Argonne National Laboratory used the fastest supercomputer on the planet to run the largest astrophysical simulation of the universe ever conducted.

The achievement was made using the Frontier supercomputer at Oak Ridge National Laboratory. The calculations set a new benchmark for cosmological hydrodynamics simulations and provide a new foundation for simulating the physics of atomic matter and dark matter simultaneously. The simulation size corresponds to surveys undertaken by large telescope observatories, a feat that until now has not been possible at this scale.

“There are two components in the universe: —which as far as we know, only interacts gravitationally—and conventional matter, or atomic matter,” said project lead Salman Habib, division director for Computational Sciences at Argonne.

Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have used machine learning and supercomputer simulations to investigate how tiny gold nanoparticles bind to blood proteins. The studies discovered that favorable nanoparticle-protein interactions can be predicted from machine learning models that are trained from atom-scale molecular dynamics simulations. The new methodology opens ways to simulate the efficacy of gold nanoparticles as targeted drug delivery systems in precision nanomedicine.

Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies critically on understanding the dynamical properties of the nano–bio interface.

Modeling the properties of the nano-bio interface is demanding since the important processes such as electronic charge transfer, or restructuring of the biomolecule surface can take place in a wide range of length and time scales, and the atomistic simulations need to be run in the appropriate aqueous environment.

Even the biggest investors often make terrible trading decisions for their portfolios.


At an AI summit in Tokyo on Wednesday, Jensen Huang and Masayoshi Son joked about how SoftBank was once Nvidia’s largest shareholder before dumping its stake. The two billionaires are now joining forces on a Japanese supercomputer. SoftBank, which until early 2019 owned 4.9% of Nvidia, has secured a favorable spot in line for the chipmaker’s latest products.\r.
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Researchers at New York University have devised a mathematical approach to predict the structures of crystals—a critical step in developing many medicines and electronic devices—in a matter of hours using only a laptop, a process that previously took a supercomputer weeks or months. Their novel framework is published in the journal Nature Communications.