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Séamus Davis, Eun-Ah Kim, Kyle Shen, Darrell Schlom, and Craig Fennie win $1M Keck Foundation Grant

A cross-campus collaboration led by Cornell engineering professor Darrell Schlom has been awarded $1 million from the W.M. Keck Foundation to transition its groundbreaking research from bold theory, based on extensive calculation, to creating a specific topological superconducting material that could pave the way to quantum computing.

“We have state-of-the-art capabilities to make artificial materials and interrogate their properties that are relevant to quantum computing, and this is a particularly exciting system for materials discovery because of its complexity and potential payoff,” said Schlom, the Herbert Fisk Johnson Professor of Industrial Chemistry in the Department of Materials Science and Engineering.

Other team members are J.C. Seamus Davis, the James Gilbert White Distinguished Professor in the Physical Sciences; Craig Fennie, associate professor of applied and engineering physics; Eun-Ah Kim, associate professor of physics; and Kyle Shen, associate professor of physics.

The team’s project is titled “A Materials-by-Design Approach to an Odd-Parity Topological Superconductor,” and its goal is to discover a material that will lay the foundation for a stable and scalable quantum computing technology.

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Erich Mueller and Shovan Dutta challenge generally accepted notion on solitons

Solitary waves – known as solitons – appear in many forms. Perhaps the most recognizable is the tsunami, which forms following a disruption on the ocean floor and can travel, unabated, at high speeds for hundreds of miles.

By definition, a soliton retains its shape while propagating at a constant velocity. But what happens when two, or more, solitons interact? The general consensus from past studies is that solitons are essentially unchanged by such an interaction and pass through one another, but physics professor Erich Mueller and graduate student Shovan Dutta have challenged that notion in a report just published in Physical Review Letters.

Their paper, “Collective Modes of a Soliton Train in a Fermi Superfluid,” was published June 29. Both men work in Cornell’s Laboratory of Atomic and Solid State Physics.

The team found something drastically different for solitons interacting in a superfluid, which forms when a gas of atoms is cooled to near absolute zero. Not only do the solitons affect one another, but they can even collide and destroy each other.

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Parpia's researchers change the fundamental frequency of an object’s motion by simply applying voltage

A professor, a postdoctoral researcher and a graduate student hop onto a trampoline.

No, it’s not the opening line of a joke. It’s a setup for the explanation of new Cornell-led research involving the wonder material graphene. A group led by Roberto De Alba, graduate student in physics, and Jeevak Parpia, professor and department chair of physics, has published a paper in Nature Nanotechnology regarding yet another application for the versatile, super-strong, super-light material.

Their paper, “Tunable phonon-cavity coupling in graphene membranes,” was published June 13 and describes the ability to use the graphene’s tension as a sort of mediator between vibrational modes, allowing for direct energy transfer from one frequency to another. De Alba was lead author.

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Eun-Ah Kim and Yi Zhang's Research Highlighted in Viewpoint

Viewpoint: Neural Networks Identify Topological Phases

A new machine-learning algorithm based on a neural network can tell a topological phase of matter from a conventional one.

A detailed characterization of phases of matter is at the forefront of research in condensed-matter and statistical physics. Although physicists have made incredible progress in the characterization of a wide variety of phases, the identification of novel topological phases remains challenging. Now, Yi Zhang and Eun-Ah Kim from Cornell University, New York [1], have taken a big-data approach to tackling this problem. In their work, thousands of microscopic “images” or “snapshots” of a phase, created using a special topography procedure, are fed into a machine-learning algorithm that is trained to decide whether these images come from a topological or a conventional phase of matter—exactly as modern computer vision algorithms are designed to tell cats from dogs in a picture.

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Figure 1: Zhang and Kim’s machine-learning algorithm for identifying a topological phase of matter involves a procedure called quantum loop topography (QLT). The procedure builds a multidimensional image from several adjacent, triangular loops located at the pixels of snapshots of the phase’s electronic density (only one such snapshot is shown here). The QLT image is then fed into a neural network that is trained to determine whether the image corresponds to a topological phase or not.

Natasha Holmes wants Students to be Critically Thinking Good Citizens

Science is about experimentation, creativity, even play. The greatest breakthroughs have come from those who pushed the known limits to ask why, how, and ultimately what if. If this is how the best science is done, then why don’t we start giving students autonomy to explore and create in the lab early in their university training? If we do, Natasha G. Holmes, Physics, says that perhaps they’ll get a taste of what it means to be a scientist early enough that they’ll choose science as a career path.

Holmes studies the teaching and learning of physics, especially in lab courses, but her work is applicable more broadly across many disciplines. “In the lab students have their hands on the equipment,” she says. “I’m looking at what they are getting or not getting out of that experience and also digging into what the lab space is actually good for. As a loftier, long-term goal, how can we provide students with transferable skills that will make them critical thinkers and good citizens?”

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Eun-Ah Kim's group works toward devising next-gen superconductor

The experimental realization of ultrathin graphene – which earned two scientists from Cambridge the Nobel Prize in physics in 2010 – has ushered in a new age in materials research.

What started with graphene has evolved to include numerous related single-atom-thick materials, which have unusual properties due to their ultra-thinness. Among them are transition metal dichalcogenides (TMDs), materials that offer several key features not available in graphene and are emerging as next-generation semiconductors.

TMDs could realize topological superconductivity and thus provide a platform for quantum computing – the ultimate goal of a Cornell research group led by Eun-Ah Kim, associate professor of physics.

“Our proposal is very realistic – that’s why it’s exciting,” Kim said of her group’s research. “We have a theoretical strategy to materialize a topological superconductor … and that will be a step toward building a quantum computer. The history of superconductivity over the last 100 years has been led by accidental discoveries. We have a proposal that’s sitting on firm principles.

“Instead of hoping for a new material that has the properties you want,” she said, “let’s go after it with insight and design principle.”

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