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UC Assistant Professor Yashar Komijani worked with an international team of experimental and theoretical physicists to explore strange metals. Photo/Andrew Higley/UC Marketing + Brand
UC Assistant Professor Yashar Komijani worked with an international team of experimental and theoretical physicists to explore strange metals. Photo/Andrew Higley/UC Marketing + Brand
Tyler O'Neal, Staff Editor ACADEMIA March 11, 2023, 9:00 am

Cincinnati physicist explores strange metals' potential as superconductors for quantum supercomputing

Physicists at the University of Cincinnati are learning more about the bizarre behavior of “strange metals,” which operate outside the normal rules of electricity.

Theoretical physicist Yashar Komijani, an assistant professor in UC’s College of Arts and Sciences, contributed to an international experiment using a strange metal made from an alloy of ytterbium, a rare earth metal. Physicists in a lab in Hyogo, Japan, fired radioactive gamma rays at the strange metal to observe its unusual electrical behavior.

Led by Hisao Kobayashi with the University of Hyogo and RIKEN, the study was published in the journal Science. The experiment revealed unusual fluctuations in the strange metal’s electrical charge.

“The idea is that in a metal, you have a sea of electrons moving in the background on a lattice of ions,” Komijani said. “But a marvelous thing happens with quantum mechanics. You can forget about the complications of the lattice of ions. Instead, they behave as if they are in a vacuum.”

Komijani for years has been exploring the mysteries of strange metals with quantum mechanics.

“You can put something in a black box and I can tell you a lot about what’s inside it without even looking at it just by measuring things like resistivity, heat capacity, and conductivity,” he said.

“But when it comes to strange metals, I have no idea why they are showing the behavior they do. The mystery is why does the charge fluctuate so slowly in a strongly correlated quantum system?” captures chrome capture 2023 2 11 26152

Strange metals are of interest to a wide range of physicists studying everything from particle physics to quantum mechanics. One reason is because of their oddly high conductivity, at least under extremely cold temperatures, which gives them potential as superconductors for quantum computing.

“The thing that is really exciting about these new results is that they provide a new insight into the inner machinery of the strange metal,” said study co-author Piers Coleman, a distinguished professor at Rutgers University.

“These metals provide the canvas for new forms of electronic matter — especially exotic and high-temperature superconductivity,” he said.

Coleman said it’s too soon to speculate about what new technologies strange metals might inspire.

“It is said that after Michael Faraday discovered electromagnetism, the British Chancellor William Gladstone asked what it would be good for,” Coleman said. “Faraday answered that while he didn't know, he was sure that one day the government would tax it.”

Faraday’s discoveries opened a world of innovation.

“We feel a bit the same about the strange metal,” Coleman said. “Metals play such a central role today — copper, the archetypal conventional metal, is in all devices, all power lines, all around us.”

Coleman said strange metals one day could be just as ubiquitous in our technology.

“The big question about strange metals - is the origin of their scale invariance — their ‘quantum criticality,’” he said. “While the experimentalists are going to try to replicate our results on other strange metals, our team at UC and Rutgers will try to fold our new discovery into a new theory of the strange metals.”

The experiment was groundbreaking in part because of the way that researchers created the gamma particles using a particle accelerator called a synchrotron.

“In Japan, they use a synchrotron as they have at CERN [the European Organization for Nuclear Research] that accelerates a proton and smashes it into a wall and it emits a gamma ray,” Komijani said. “So they have an on-demand source of gamma rays without using radioactive material.”

Researchers used spectroscopy to study the effects of gamma rays on the strange metal.

Researchers also examined the speed of the metal’s electrical charge fluctuations, which take just a nanosecond — a billionth of a second. That might seem incredibly fast, Komijani said.

“However, in the quantum world, a nanosecond is an eternity,” he said. “For a long time, we have been wondering why these fluctuations are actually so slow. We came up with a theory with collaborators that there might be vibrations of the lattice and indeed that was the case.”

The study was funded in part by the National Science Foundation and the Department of Energy.

Yuan Yao Assistant Professor of Industrial Ecology and Sustainable Systems
Yuan Yao Assistant Professor of Industrial Ecology and Sustainable Systems

Yale prof Yao investigates the use of ML for the sustainable development of biomass

Tyler O'Neal, Staff Editor ACADEMIA March 9, 2023, 9:00 am

Biomass is widely considered a renewable alternative to fossil fuels, and many experts say it can play a critical role in combating climate change. Biomass stores carbon and can be turned into bio-based products and energy that can be used to improve soil, treat wastewater, and produce renewable feedstock. 

Yet large-scale production of it has been limited due to economic constraints and challenges to optimizing and controlling biomass conversion. Yao Biomass 687504388 587d0

A new study led by Yale School of the Environment’s Yuan Yao, assistant professor of industrial ecology and sustainable systems, and doctoral student Hannah Szu-Han Wang, analyzed current machine learning applications for biomass and biomass-derived materials (BDM) to determine if machine learning is advancing the research and development of biomass products. The study authors found that machine learning has not been applied across the entire life cycle of BDM, limiting its ability for growth.

Yao’s research investigates how emerging technologies and industrial development will affect the environment with a focus on bio-economy and sustainable production. Wang worked in the production of biomaterials during her master’s research. The two researchers said they were interested in pursuing this study to find out if machine learning could help with best practices for creating BDM, a chief component of a bio-based economy, as well as predicting their performance as sustainable materials.

“There are so many combinations of biomass feedstock, conversion technologies, and BDM applications. If we want to try each combination using the traditional trial-and-error experimental approach, this will take a lot of time, labor, effort, and energy. We already generate a lot of data from these past experiments, so we are asking, can we apply machine learning to help us to figure out how we can better design BDM?" Yao explains.

For the study published in Resources, Conservation, and Recycling, Yao and Wang reviewed more than 50 papers published since 2008 to understand the capabilities, current limitations, and future potential of machine learning in supporting sustainable development and applications of BDM. What they found is that while a few studies applied machine learning to address data challenges for life cycle assessment, most studies only applied machine learning to predict and optimize the technical performance of biomass conversion and applications. None reviewed machine learning applications across the entire lifecycle, from biomass cultivation to BDM production and end-use applications.

“Most studies are applying machine learning to just a very small part of the entire lifecycle of BDM,” Yao says. “We argue that if you want to incorporate sustainability into the development of this material, we need to consider the entire lifecycle of the materials, from how they are generated to their potential environmental impact. We believe machine learning has the potential to support sustainability-informed design for biomass-derived materials.”

Wang said the study has led to further research on data gaps in machine learning on biomass-derived materials.

“We found a future direction that people have not yet explored regarding sustainability assessments for BDM. There needs to be a full pathway prediction to enhance our understanding of how various factors regarding BDM interact and contribute to sustainability,” she says.

Zooming Through a Simulated Universe

Tyler O'Neal, Staff Editor ACADEMIA March 8, 2023, 11:59 am
This video begins by showing the most distant galaxies in the simulated deep field image in red. As it zooms out, layers of nearer (yellow and white) galaxies are added to the frame. By studying different cosmic epochs, Roman will be able to trace the universe's expansion history, study how galaxies developed over time, and much more. Credit: Caltech-IPAC/R. Hurt and M. Troxel

Read more https://www.supercomputingonline.com/gallery/category/news/zooming-through-a-simulated-universe

  1. SETI Institute’s NAI team paves the way for machine learning to assist scientists in the search for biosignatures in the Universe
  2. Can AI help find life on Mars or Icy Worlds?

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