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A research team has provided the first experimental proof that flat electronic bands in a kagome superconductor are active and directly shape electronic and magnetic behaviors.

Researchers from Rice University, working with international partners, have found the first clear evidence of active flat electronic bands within a kagome superconductor. The discovery marks an important step toward creating new strategies for designing quantum materials, including superconductors, topological insulators, and spin-based electronics, which could play a central role in advancing future electronics and computing.

The findings, published on August 14 in Nature Communications, focus on the chromium-based kagome metal CsCr₃Sb₅, a material that becomes superconducting when placed under pressure.

Kagome metals are defined by their unique two-dimensional lattice of corner-sharing triangles. Recent theories have suggested that these structures can host compact molecular orbitals, or standing-wave patterns of electrons, which may enable unconventional superconductivity and unusual magnetic states driven by electron correlation effects.

In most known materials, such flat bands are positioned too far from the relevant energy levels to influence behavior. In CsCr₃Sb₅, however, they play an active role and directly shape the properties of the material.

Pengcheng Dai, Ming Yi, and Qimiao Si of Rice’s Department of Physics and Astronomy and Smalley-Curl Institute, along with Di-Jing Huang of Taiwan’s National Synchrotron Radiation Research Center, led the study.

“Our results confirm a surprising theoretical prediction and establish a pathway for engineering exotic superconductivity through chemical and structural control,” said Dai, the Sam and Helen Worden Professor of Physics and Astronomy.

The finding provides experimental proof for ideas that had only existed in theoretical models. It also shows how the intricate geometry of kagome lattices can be used as a design tool for controlling the behavior of electrons in solids.

“By identifying active flat bands, we’ve demonstrated a direct connection between lattice geometry and emergent quantum states,” said Yi, an associate professor of physics and astronomy.

Experimental Techniques and Findings

The research team employed two advanced synchrotron techniques alongside theoretical modeling to investigate the presence of active standing-wave electron modes. They used angle-resolved photoemission spectroscopy (ARPES) to map electrons emitted under synchrotron light, revealing distinct signatures associated with compact molecular orbitals. Resonant inelastic X-ray scattering (RIXS) measured magnetic excitations linked to these electronic modes.

“The ARPES and RIXS results of our collaborative team give a consistent picture that flat bands here are not passive spectators but active participants in shaping the magnetic and electronic landscape,” said Si, the Harry C. and Olga K. Wiess Professor of Physics and Astronomy, “This is amazing to see given that, until now, we were only able to see such features in abstract theoretical models.”

Theoretical support was provided by analyzing the effect of strong correlations starting from a custom-built electronic lattice model, which replicated the observed features and guided the interpretation of results. Fang Xie, a Rice Academy Junior Fellow and co-first author, led that portion of the study. 

Obtaining such precise data required unusually large and pure crystals of CsCr₃Sb₅, synthesized using a refined method that produced samples 100 times larger than previous efforts, said Zehao Wang, a Rice graduate student and co-first author.

The work underscores the potential of interdisciplinary research across fields of study, said Yucheng Guo, a Rice graduate student and co-first author who led the ARPES work. 

“This work was possible due to the collaboration that consisted of materials design, synthesis, electron and magnetic spectroscopy characterization, and theory,” Guo said.

Reference: “Spin excitations and flat electronic bands in a Cr-based kagome superconductor” by Zehao Wang, Yucheng Guo, Hsiao-Yu Huang, Fang Xie, Yuefei Huang, Bin Gao, Ji Seop Oh, Han Wu, Jun Okamoto, Ganesha Channagowdra, Chien-Te Chen, Feng Ye, Xingye Lu, Zhaoyu Liu, Zheng Ren, Yuan Fang, Yiming Wang, Ananya Biswas, Yichen Zhang, Ziqin Yue, Cheng Hu, Chris Jozwiak, Aaron Bostwick, Eli Rotenberg, Makoto Hashimoto, Donghui Lu, Junichiro Kono, Jiun-Haw Chu, Boris I. Yakobson, Robert J. Birgeneau, Guang-Han Cao, Atsushi Fujimori, Di-Jing Huang, Qimiao Si, Ming Yi and Pengcheng Dai, 14 August 2025, Nature Communications.
DOI: 10.1038/s41467-025-62298-5

Funding: U.S. Department of Energy, Welch Foundation, Gordon and Betty Moore Foundation, Air Force Office of Scientific Research, U.S. National Science Foundation

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A team of researchers at École Polytechnique Fédérale de Lausanne — or the Swiss Federal Institute of Technology in Lausanne — recently created a database to help scientists monitor wildlife without ever entering their habitats. Called MammAlps, the “richly annotated, multi-view, multimodal wildlife behavior dataset” is a way for scientists to better understand wildlife behavior, according to an EPFL article published on Phys.org.

To create the dataset, researchers set up nine cameras across the Swiss National Park, capturing more than 43 hours of raw footage. They then trimmed down this footage into 8 1/2 hours of highly detailed, labeled clips with the help of artificial intelligence. 

Each clip includes in-the-moment annotations of wildlife behaviors across two levels: broader activities such as “foraging” or “playing,” and more detailed actions such as “sniffing” or “running.” As an animal’s behavior changes during a clip, the labels change accordingly, creating a real-time behavioral timeline.

However, the database doesn’t just catalog video. It also includes audio recordings, environmental maps of natural features such as rivers and vegetation, and even documentation of weather conditions at the time.

“This multi-modal approach gives us a more complete picture of wildlife behavior,” EPFL professor and project supervisor Alexander Mathis said in the article.

While this detailed dataset could be a breakthrough in conservation, it’s worth noting that the use of AI comes at an environmental cost. Data centers powering AI require large amounts of electricity — often from dirty energy sources — and billions of gallons of water for cooling. AI hardware also relies on rare minerals and elements, which are often mined unethically and unsustainably, per the United Nations Environment Programme.

The growing demands of AI are expected to contribute to the doubling of data center power consumption from 2022 to 2026. 

Still, researchers believe MammAlps could help conservationists to quickly identify significant wildlife behaviors in hundreds of hours of wildlife footage. By training AI on the dataset, the technology could automatically scan new recordings and extract the most relevant segments. This could help spot changes in animal behavior caused by climate shifts, human activity, or even disease, leading to more effective protection strategies — all without disturbing animals in the wild.

The data collection isn’t over, either. The team is currently processing data collected in 2024 and plans to conduct more observations in 2025. These additional surveys will help expand recordings for rare species, including alpine hares and lynx. Additionally, researchers hope to analyze wildlife behavior over multiple seasons by expanding their observations.

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