Fang Liu at Emory
@fangliuemory.bsky.social
A detailed story about this work is also available on Emory News: news.emory.edu/stories/2025...
New AI tool set to speed quest for advanced superconductors | Emory University | Atlanta GA
Using artificial intelligence shortens the time to identify complex quantum phases in materials from months to minutes, finds a new study led by Emory scientists. The breakthrough could speed up resea...
news.emory.edu
April 10, 2025 at 10:39 PM
A detailed story about this work is also available on Emory News: news.emory.edu/stories/2025...
The work is published today on Newton: doi.org/10.1016/j.ne...
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doi.org
April 10, 2025 at 10:39 PM
The work is published today on Newton: doi.org/10.1016/j.ne...
Our model shows 97.6% accuracy in identifying thermodynamic phase transitions of cuprate superconductors with strong superconducting fluctuations. These advances open new directions for understanding and harnessing complex quantum many-body states and phase transitions in quantum materials. 🧵4/6
April 10, 2025 at 10:39 PM
Our model shows 97.6% accuracy in identifying thermodynamic phase transitions of cuprate superconductors with strong superconducting fluctuations. These advances open new directions for understanding and harnessing complex quantum many-body states and phase transitions in quantum materials. 🧵4/6
The model recognizes subtle spectral cues, unveils transitions under heavy fluctuations, and enables rapid detection of emergent phases in quantum materials, fueling exploration of strongly correlated systems and expediting the discovery of next-generation superconductors. 🧵3/6
April 10, 2025 at 10:39 PM
The model recognizes subtle spectral cues, unveils transitions under heavy fluctuations, and enables rapid detection of emergent phases in quantum materials, fueling exploration of strongly correlated systems and expediting the discovery of next-generation superconductors. 🧵3/6
In correlated materials, strong quantum fluctuations hamper conventional spectral diagnosis of phase transitions, as the usual “energy gap” is no longer a reliable marker. We employ ML to parse single-shot photoemission spectra and pinpoint long-range superconducting order. 🧵2/6
April 10, 2025 at 10:39 PM
In correlated materials, strong quantum fluctuations hamper conventional spectral diagnosis of phase transitions, as the usual “energy gap” is no longer a reliable marker. We employ ML to parse single-shot photoemission spectra and pinpoint long-range superconducting order. 🧵2/6