New research describes a phase in between the liquid and the solid for electron crystals – a liquid crystal state. https://news.cornell.edu/stories/2022/11/physicist-identifies-how-electr...

A collaboration led by Eun-Ah Kim, professor in the College of Art and Sciences, employed machine learning to analyze a massive dataset from a quantum metal to settle a debate about this material. https://news.cornell.edu/stories/2022/06/harnessing-machine-learning-ana...

Six Arts and Sciences faculty members focusing on mathematics and theoretical physics were announced as the 2022 Simons Fellows. The program enables recipients to focus on research by extending academic leaves from one term to a full year. https://news.cornell.edu/stories/2022/03/six-professors-named-2022-simon...

Physicist Eun-Ah Kim is leading the way toward applications of quantum mechanics, including the discovery of new quantum materials and the development of quantum computing. https://news.cornell.edu/stories/2021/09/grant-funds-machine-learning-di...

An interdisciplinary team developed a tool to parse quantum matter and make crucial distinctions in the data, helping scientists unravel the most confounding phenomena in the subatomic realm. https://news.cornell.edu/stories/2021/07/machine-learning-tool-sorts-nua...

Machine-learning algorithms are helping to unravel the quantum behaviour of a type of superconductor that has baffled physicists for decades.

Researchers used artificial intelligence to spot hidden order in images of a bizarre state in high-temperature superconductors.

The result, published in a pre-print1 on the arXiv earlier last month, supports one theory in a decades-long attempt to understand these materials.

The study also represents the first time that machine learning has been successfully used to make sense of experimental data on quantum matter, said Eun-Ah Kim at Cornell University in Ithaca, New York, who presented the work at the Materials and Mechanisms of Superconductivity and High Temperature Superconductivity meeting in Beijing in August.

In the long term, machine learning could boost efforts to spot simple patterns in other noisy and chaotic experimental systems, such as quantum spin liquids, which could form the basis of a future exotic type of quantum computer. https://www.nature.com/articles/d41586-018-06144-3

Journal Club for Condensed Matter Physics - A Monthly Selection of Interesting Papers by Distiguished Correspondents

Pomeranchuk instability of composite Fermi liquid Authors: K. Lee, J. Shao, E-A Kim, F.D.M. Haldane, and E. H. Rezai arXiv:1802.08261 

https://www.condmatjclub.org/uploads/2018/08/JCCM_August_2018_03.pdf

Physicists in the US have used machine learning to determine the phase diagram of a system of 12 idealized quantum particles to a higher precision than ever before. The work was done by Eun-Ah Kim of Cornell University and colleagues who say that they are probably the first to use machine learning algorithms to uncover “information beyond conventional knowledge” of condensed matter physics. https://physicsworld.com/a/machine-learning-reveals-quantum-phases-of-ma...

The Keck Foundation announced in early July that it had awarded $1 million to a Cornell cross-campus collaboration of professors in engineering and physics aimed at turning theory into reality – namely, creating a specific topological superconducting material that could help pave the way to quantum computing.

The idea that sparked the group’s winning proposal came out of the group led by Eun-Ah Kim, associate professor of physics, and is now the first published research from a member of that five-member group.

https://news.cornell.edu/stories/2017/11/keck-funded-group-proposes-new-...

In CornellResearch's article by Caitlin Hayes, Eun-Ah Kim shares how she perceives and studies the secretive interactive behaviour of electrons. Read More in The Social Life of Electrons