Embedded Files
06/09/2022 - Unconventional Superconductivity from Flat Bands
Kai Sun, Professor, Department of Physics
In this talk, we study unconventional superconductivity in flat band systems, i.e. a system where the kinetic energy of an electron is almost a constant independent of electron momentum. We find that due to an emergent SU(2) symmetry, the ideal flat-band limit is exactly solvable, and the system shall have infinite many degenerate ground states. In a real system, energy bands is not infinitely flat, and the finite band width lifts this infinite degeneracy, resulting in an unconventional superconductor. We will compare this superconductor with conventional (BCS) superconductors to demonstrate its unconventional features, such as diverging compressibility and pseudo-gap.
06/16/2022 - Cosmology Using Galaxy Surveys
Otávio Alves, 2nd Year, Department of Physics
The observed spacial distribution and shapes of galaxies encode a wealth of information about the evolution of our Universe and the fundamental physics governing it. In this talk I will review a few ways in which data from galaxy surveys can be used to probe fundamental physics such as deviations from general relativity, mass of neutrinos and the nature of the dark sector. Methods I will review include weak gravitational lensing and galaxy clustering.
06/23/2022 - Z-Pinch Research in Pursuit of Fusion Energy
Akash Shah, 5th Year, Applied Physics; Nuclear Engineering and Radiological Sciences
Fusion energy research is primarily aimed at creating conditions in a plasma in which the energy output of the system exceeds the energy input. Various mechanisms have been developed to study such systems including magnetic confinement schemes (e.g., tokamaks and stellarators) and inertial confinement schemes (e.g., direct-drive fusion, indirect-drive fusion, and magnetized-liner fusion). The magnetized-liner inertial fusion or MagLIF experiment, developed for the Z machine at Sandia National Laboratories, uses a combination of energetic lasers (2 KJ) and powerful current pulses (80 TW) to heat and compress fusion fuel with an axisymmetric geometry called a z-pinch. University-scale z-pinch experiments such as on the MAIZE Linear Transformer Driver (LTD) at the University of Michigan can inform the high-value experiments carried out on the Z machine. MAIZE allows for studying z-pinch dynamics and energetics such as the effects of initial conditions on instability formation, current distribution, and x-ray and fusion neutron output in the z-pinch. This talk presents a general overview of the state of fusion research, focuses on MagLIF as an inertial confinement fusion scheme, and examines university-scale z-pinch experiments as tools to study the complex physics involved.
06/30/2022 - Machine Learning in Cosmology
Ismael Mendoza, 4th Year, Department of Physics
In the upcoming decades, we will have the opportunity to solve some of the biggest questions about our universe by taking advantage of the huge amounts of data produced by upcoming state-of-the-art cosmological experiments. In order to harness the full statistical power of this data, we will need to develop scalable and accurate algorithms that can extract its maximal information. Recent advances in Machine Learning have demonstrated its ability to overcome the computational bottlenecks of traditional statistical techniques and even achieve better performance when analyzing cosmology data. In this talk, I will give a brief overview of the open problems in cosmology, motivate how Machine Learning (ML) could help us answer these by enabling novel analyses of upcoming cosmological surveys, and give a specific application of ML enabling probabilistic detection and measurement of galaxy images.
07/14/2022 - Measuring the chiroptical response of single molecules in the near field of plasmonic nanoparticles
Saaj Chattopadhyay 3rd Year, Applied Physics
The chirality of biomolecules is a good indicator of their structure and function. Fluorescence-detected circular dichroism remains a primary detection scheme for chirality due to its sensitivity. However, since most biomolecules have a low dissymmetry factor in the visible range (~103), single-molecule detection of chirality is challenging even for fluorescent molecules. To achieve single-molecule fluorescence-detected circular dichroism, we are leveraging plasmonic nanoparticle substrates, which focus incident plane waves into locally varying near fields, to enhance the dissymmetry factor. By varying the substrate design and the incident polarization of the plane wave, we are optimizing the electric field density and optical chirality to enhance the differential fluorescence intensity of proximal chiral biomolecules. Because the electromagnetic landscape varies on the nanometer scale, the differential signal will be most enhanced within a sub-diffraction-limited area on the sample. We use single-molecule super-resolution microscopy to experimentally access these hotspots. I will present the chiroptical interactions of single pairs of cyanine dyes (aligned into J-aggregates by a double-stranded backbone to form right-handed fluorescent biomolecules) with chiral and achiral gold nanoparticles. In this talk, I will discuss the design of the experimental setup and use full-field simulations to determine the electromagnetic near field produced by varying the incident polarization at the substrate.
07/21/2022 - Learn to design: from optimization to deep learning and reinforcement learning
Taigao Ma, 3rd Year, Department of Physics
Learn to design: from optimization to deep learning and reinforcement learning. Abstract: Designing physical structures and devices to achieve desirable performance is an important study in many disciplines, including physics and engineering. However, the design process is non-trivial because of the large design spaces as well as the non-unique optimal design. Usually, the design process requires an iterative trial-and-error process conducted by human experts through extensive simulations or experiments, which wastes much time and effort. The recent development of computer science has reshaped this research domain. In this talk, I will give a brief overview of these design methods, with a special focus on designing optical and photonic structures and devices. In this talk, I will briefly discuss three parts: 1) Traditional optimization methods; 2) Deep learning methods that use the neural networks as function approximators to speed up the evaluations and guide for optimization; 3) Reinforcement learning methods that can efficiently extrapolate in the design space and provide new design thoughts. One specific example will be discussed in each part.
07/28/2022 - Linear Maximum Distance Separable Coded Matrix Inversion
Neophytos Charalambides, 5rd Year, EECS
A cumbersome operation in physics, numerical analysis and linear algebra, optimization and machine learning, is inverting large full-rank matrices. In this paper, we propose a coded computing approach for recovering matrix inverse approximations. We first present an approximate matrix inversion algorithm which does not require a matrix factorization, but uses a black-box least squares optimization solver as a subroutine, to give an estimate of the inverse of real full-rank matrices. We then present a distributed framework for which our algorithm can be implemented, and show how we can leverage from sparsest-balanced MDS generator matrices to devise inverse coded computing schemes. We focus on balanced Reed-Solomon codes, which are optimal in terms of computational load; and communication from the workers to the master server. We also discuss how our algorithms can be used to compute the pseudoinverse of a full-rank matrix, and how the communication can be secured from eavesdroppers.
08/04/2022 - Improvement of Constant pH Molecular Dynamics on the example of Folate Receptor
Stanislav Cherepanov, 2nd Year, Biophysics
Folate receptor is one of the possible targets for anticancer drugs, and this observation is supported by the current use of antifolates in various cancer therapies. In the process of folic acid consumption by a cell, the receptor goes through various stages of endocytosis with changing pH. Moreover, it is known that tumor cells have a more acidic pH than normal cells. However, the pH dependence binding and release of folate and its difference from antifolates is not well understood at this moment. Furthermore, identification of key amino acid residues within folate receptor responsible for the pH dependent conformational changes observed within the apo and holo structures of folate receptor will further an effort in designing new antifolates with specific properties. Here, we study pKa shifts of amino acids at different stages using Constant pH Molecular Dynamics and their effect on different molecule uptake by the folate receptor.
08/11/2022 - Proton Structure and Hadron Formation
Dillon Fitzgerald, 5th Year, Department of Physics
The structure of hadrons is incredibly rich and complex, despite the proton being one of the most familiar building blocks of matter. It is of great interest to characterize hadronic bound states in terms of their constituent quark and gluon degrees of freedom, such as spin, momentum, position, and flavor. Similarly, it is of great interest to understand how hadronic bound states form during the evolution of high energy collisions in scattering processes involving outgoing quarks or gluons. Both are central questions in Quantum Chromodynamics (QCD) that the Aidala group is investigating with a number of unique datasets from PHENIX at the Relativistic Heavy Ion Collider (RHIC), and LHCb at the Large Hadron Collider (LHC). RHIC is the world's only polarized proton collider, allowing for a wealth of unique spin-spin and spin-momentum correlation measurements between protons and their constituents, while LHCb is a forward spectrometer with excellent beauty-quark tagging and particle identification capabilities, making it ideal for measurements elucidating hadron formation mechanisms. In addition, the sPHENIX experiment at RHIC, as well as the Electron-Ion Collider (EIC), offer a very promising future for answering these fundamental questions. In this talk, I will provide some background on these open questions in QCD, and discuss various measurements from the Aidala research group with an emphasis on how they improve our understanding.
08/18/2022 - Coherent Imaging Spectroscopy of van-der-Waals materials
Torben Purz, 4th Year, Department of Physics
Transition metal dichalcogenides (TMDs) have received considerable attention in the past decade for their optoelectronic applications in photovoltaics, lasers, and quantum information. In the monolayer limit, these materials exhibit extraordinary properties including efficient light-matter coupling, ultrafast charge transfer, long-lived interlayer bound electron-hole pairs (excitons) which are stable at room temperature, and many-body excitonic interactions. In this talk I will present how multi-dimensional coherent spectroscopy (MDCS) allows us to study coherent and incoherent coupling between excitons in TMD heterostructures. I will further show how the development of a novel lock-in amplifier allows us to combine MDCS with imaging and study the changes of key sample properties for quantum information applications across TMD monolayers and heterostructures. Lastly, the talk will cover how to accelerate nonlinear imaging techniques to implement them in fabrication settings with experimental results supporting the advantage of four-wave mixing based imaging over conventional material characterization techniques.
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