Abstract
Coming Soon.
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Coming Soon.
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In this talk I will discuss ongoing efforts at UChicago to explore matter made of light. I will begin with a broad introduction to the challenges associated with making matter from photons, focusing specifically on (1) how to trap photons and imbue them with mass and charge; (2) how to induce photons to collide with one another; and (3) how to drive photons to order, by cooling or otherwise. I will then provide as examples two state-of-the-art photonic quantum matter platforms: microwave photons coupled to superconducting resonators and transmon qubits, and optical photons trapped in multimode optical cavities and made to interact through Rydberg-dressing. In each case I will describe a synthetic material created in that platform: a Mott insulator of microwave photons, stabilized by coupling to an engineered, non-Markovian reservoir, and a Laughlin molecule of optical photons prepared by scattering photons through the lowest-Landau-level optical cavity. Indeed, building materials photon-by-photon will provide us with a unique opportunity to learn what all of the above words mean, and why they are important for quantum-materials science. Finally, I will conclude with my view of the broad prospects of photonic matter in particular, and of synthetic matter more generally.
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Mass spectrometry-based methods such as covalent labeling, surface induced dissociation (SID) or ion mobility (IM) are increasingly used to obtain information about protein structure. However, in contrast to other high-resolution structure determination methods, this information is not sufficient to deduce all atom coordinates and can only inform on certain elements of structure, such as solvent exposure of individual residues, properties of protein-protein interfaces or protein shape. Computational methods are needed to predict high-resolution protein structures from the mass spectrometry (MS) data.
Our group develops algorithms within the Rosetta software package that use mass spectrometry data to guide protein structure prediction. These algorithms can incorporate several different types of mass spectrometry data, such as covalent labeling, surface induced dissociation, and ion mobility. We developed scoring functions that assess the agreement of residue exposure with covalent labeling data, the agreement of protein-protein interface energies with SID data and the agreement of protein model shapes with collision cross section (CCS) IM measurements. We subsequently rescored Rosetta models generated with de novo protein folding and protein-protein docking and we were able to accurately predict protein structure from MS labeling, SID and IM data.
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Magnetism is a fascinating phenomenon: it is rooted in relativistic quantum mechanics and yet an integral component of the technologies we use every day. In magnetic insulators, where atomic-scale magnetic dipoles carried by electrons are closely bound to a crystal lattice, novel phases of matter, sometimes with no classical analogues, are possible. Chief among these phases are spin-liquids, in which strong fluctuations of magnetic dipoles (spins) preclude conventional magnetic order even for temperatures very low compared to the interaction scale between spins. Such exotic magnetic matter is of great fundamental interest because it features a wealth of coherence and entanglement phenomena – the hallmarks of the quantum world – and is often amenable to theoretical and computational predictions. In this talk, I will present experimental research that brings together materials chemistry, neutron scattering and computer modeling to search for spin-liquids in a range of compounds which crystal structures contain two- and three-dimensional simplices. My talk will emphasize the importance of neutron scattering instrumentation at large-scale facilities to probe complex materials behavior in which chemical disorder, geometrical frustration and quantum fluctuations interplay to stabilize – or destroy – spin-liquid physics.
This research is supported by the U.S. Department of Energy under award DE-SC-0018660 and the National Science Foundation under award NSF-DMR-1750186.
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Georgia Institute of
Technology
North Avenue, Atlanta, GA 30332
404.894.2000