2021 REU Program

Nonequilibrium and Disorder Properties in Relaxor and Regular Ferroelectrics

Faculty Advisor: Eugene V Colla

Relaxors are dielectric materials with short-range ferroelectric order, but which do not develop long range ferroelectric order spontaneously at low electric field. Since the short-range order can re-orient in response to applied fields, they keep those high dielectric and piezoelectric constants over a broad temperature range, allowing them to be used for practical electromechanical transducers in many applications. Some regular ferroelectric like BaTiO3, KH2P04 and KD2PO4 show also some nonequilibrium properties similar to those observed in ferroelectric relaxors. These materials unlike the relaxors undergo the phase transition to long-range ferroelectric state and form the macroscopic domain pattern typical to all other ferroelectric. In the same time below critical temperature they show large low field dielectric susceptibility which cannot be explained by macroscopic domain dynamics. This high dielectric susceptibility state is not equilibrium and is the subject for aging -koll decreasing of the susceptibility in time. This project includes working on sample preparation, and measuring and analyzing dielectric and pyroelectric properties, investigation of ferroelectric domains using polarizing microscope. No prior experience necessary. This is a great hands-on experimental physics opportunity! [Experimental]

Far-from-equilibrium relativistic hydrodynamics

Faculty Advisor: Jorge Jose Leite Noronha, Jr

Relativistic fluid dynamics plays a fundamental role in our understanding of the quark-gluon plasma formed in heavy-ion collisions. In this project the student will learn about relativistic viscous fluids, their formulation and current theoretical challenges, and also their applications in heavy-ion collisions. Numerical solutions will be found (using MATHEMATICA or C++ as programing languages) to understand the differences between current formulations of viscous relativistic fluids using highly symmetric flow profiles. The emergence of novel hydrodynamic attractor behavior in such systems, and their potential role in heavy-ion collisions, will be discussed. [High-Energy Nuclear Theory]  

An Investigation in Quantum Nonlocality

Faculty Advisor: Eric Chitambar

Quantum nonlocality is a highly non-classical feature of multi-part quantum systems.  Recently much work has been devoted to understanding nonlocality as a resource in quantum information processing.  A basic and challenging question arises as to which quantum states can generate nonlocal correlations by local measurements, and which always yield measurement statistics that can be simulated by a local hidden variable model. In this research project, the student will first learn the basic definitions and mathematical properties of Bell inequalities and quantum nonlocality.  One research goal will then be to find quantum violations of Bell Inequalities in different scenarios.  This research will involve performing both analytical calculations using pen and paper along with numerical optimizations. [65% Theory/ 35% Computational]

Synthetic topological metamaterials

Faculty Advisor: Bryce Gadway

This project involves the construction of an array of "synthetically" coupled mechanical oscillators for the study of new types of artificial "materials" with designer properties related to nontrivial topology, interactions, and non-Newtonian behavior. The student will learn ideas from the areas of quantum simulation, AMO physics, and condensed matter physics, will gain experience in experimental control techniques and electronics, along with Labview and Mathematica or Python. [Hybrid of experimental/computational]

Rare-earth atoms in solids for quantum light-matter interfaces

Faculty Advisor: Elizabeth Goldschmidt

Quantum light-matter interfaces are a vital component in optical quantum information systems. Reversibly mapping quantum information between light and atoms or coherent atom-like emitters is a major challenge in the field. We work with rare-earth atoms in solids due to their excellent coherence properties and potential for integration with photonic systems. We use optics, lasers, cryogenic systems, rf and microwave electronics and a variety of other experimental tools, but no prior experience is necessary. Some familiarity with python is helpful for the experimental control, but not required. [Primarily Experimental] - Physical Only

Quantum control techniques

Faculty Advisor: Wolfgang Pfaff

Our laboratory performs research on the generation, distribution, and protection of quantum states in superconducting qubit networks. This project is aimed to investigate how classical computers can help us to optimize tasks such as quantum state generation, entanglement generation, and quantum state detection. The student will first numerically simulate how different control and measurement strategies are predicted to perform on simulated quantum systems. We will aim to verify predicted performance in an experiment. Key aspects and things students will learn: design of qubit control and measurement pulses; numerical optimization techniques; physical implementation of quantum protocols. Required skills for this project: Elementary quantum mechanics; experience in at least one programming language, preferably python. This project can be done virtually.

Topics in Computational Astrophysics

Faculty Advisor: Charles Forbes Gammie

The Gammie Group works on a broad range of topics in theoretical and computational astrophysics, including black hole astrophysics, the Event Horizon Telescope, the formation of the Moon, turbulence, and numerical techniques.  We have projects available in visualization of simulations, analysis of simulations (using python scripts), testing of simulation codes, and modeling of Event Horizon Telescope sources.  There are no prerequisites to join the group.  We are looking for a variety of students, some with computational expertise, some with deep backgrounds in physics, some with the ability to write clearly, and some who bring only a surplus of enthusiasm.

Applying machine learning to validate rules of life for a minimal cell

Faculty Advisor: Zaida Ann Luthey-Schulten

The minimal cell, JCVI-syn3A (Science 2016, eLife 2019, Frontiers in Molecular Biosciences 2019), represents a new paradigm for the role of stochasticity in cellular functions. It was synthesized at the J. Craig Venter Institute (JCVI), contains the smallest bacterial genome to-date (493 genes), yet is capable of carrying out all essential cellular functions including cell division within 100 minutes. We have developed  kinetic models  of all cellular subsystems and their interactions with the environment for this “model” organism and used them to determine the rules of life for a minimal bacterial cell.  The goals of this research and the associated REU projects are to validate theoretically and experimentally the basic principles of life. To accomplish these goals requires the analysis of several factors and events occurring in a population of minimal cells over the average cell cycle. A partial list includes: What are the noise-contributions to the cellular functions of metabolism, genetic information processing and cell division?  What percentage of cells have a high fraction of near zero-mRNAs for genes with low expression? Is there a scaling law between transcription of genes and volume? What is are the correlations between the function and timing of the various cellular networks? Can machine learning identify additional correlations among the time series data?  Many of these questions can be probed with our computational model simulated with our GPU-based software Lattice MIcrobes and validated with further experiments being performed by our collaborators at UIUC, JHU, the technical universities at Leiden and Dresden while others require the development of new statistical mechanical treatments to bridge the multiple scales and processes. The 2019 REU student working in the Luthey-Schulten laboratory was one of the co-authors of the Frontiers 2019 article in which he modeled the stochastic processes that initiated DNA replication in the minimal cell. He learned to carry out stochastic simulations using the direct Gillespie method implemented into our Lattice Microbe software. REU students should have a knowledge of statistical mechanics, kinetics, and a computational methodology such as python. They will learn to present their results using Jupyter Python notebooks which allow them to be easily shared with experimentalist. [Primarily computational and theoretical with integration of experimental data]  

Free Volume Reduction

Faculty Advisor: Martin Gruebele

Activity assays of the enzyme PGK to determine how its binding is affected by inert crowders. Free volume reduction is a physical mechanism by which cells can tune access of substrates to biomolecules. PGK is an enzyme with two domains, and thus particularly sensitive to volume effects inside the cell.

Development of a Zero Degree Calorimeter and Reaction Plane Detector for the ATLAS Experiment at the LHC

Faculty Advisor: Matthias Grosse Perdekamp

The ATLAS experiment at the Large Hadron Collider at CERN uses Pb-Pb collisions to study the quark gluon plasma (QGP). The ATLAS Zero Degree Calorimeter (ZDC) and the Reaction Plane Detector (RPD) observe neutrons evaporating from the non-interacting nuclear fragments emerging from Pb-Pb ion collisions. Through this observation the event geometry of the nuclear collisions can be determined. The current detectors operate at radiation doses beyond the levels tolerable by existing detector technology and requires regular repair. Our group is developing novel ZDC and RPD detectors based on advanced fused silica materials that tolerate very high radiation doses. The REU project will focus on the development of machine learning based data analysis techniques for the RPD and ZDC data. [Primarily Experimental]

Finding the smallest droplet of the most perfect fluid

Faculty Advisor: Jaki Noronha-Hostler

Using large particle accelerators that collide specs of lead at 99.9999% the speed of light, nuclear physicists can create the tiniest droplets of fluid known to humanity (~10^-22 meters). This tiny fluid is known as the Quark-Gluon Plasma and it has the smallest viscosity of any fluid in nature (essentially the completely opposite of tar or honey). In order to test the limits of the size of this tiny fluid, theorists run relativistic hydrodynamic simulations and compare them directly to experimental observables. In this project, the student will learn about relativistic hydrodynamics and run simulations of this very tiny fluid on high-performance computers. We will make direct comparisons to experimental data at the Large Hadron collider. No previous computational knowledge is required. Throughout the project the student will learn to code in C++ and will learn about relativistic fluids, Big Data statistical analysis techniques, and modern nuclear physics. [Theoretical/Computational physics in high-energy nuclear theory]

Collaboration, belonging, and success in introductory physics

Faculty Advisor: Eric Kuo

Productive collaboration and a student’s sense of belonging are two elements that can contribute to success in physics courses.  This project aims to uncover how successful collaboration and belonging arises in introductory physics, with the goal of informing new instructional approaches for physics education.  Research activities will consist of data processing and analysis of classroom video observations, course assignments performance, and/or survey responses. Researchers on this project can expect to gain a familiarity with current issues in Physics Education Research and experience with modern education research methodologies, such as clinical interviewing, discourse analysis, experimental design, and statistical analysis. No prior experience is necessary, but an interest in the mechanisms governing physics learning and academic success is desirable.

Super-resolution microscopy to capture molecular dynamics in real time in living cells

Faculty Advisor: Sangjin Kim

Proteins are essential for life. Often a protein’s function relies on its dynamics, but it is technically challenging to measure protein dynamics, especially in living cells. This project involves using super-resolution microscopy to measure  protein dynamics in real-time inside cells. The REU student will use a new super-resolution fluorescence microscopy system and learn about image analysis. This experimental work will be combined with Brownian dynamics simulations to interpret experimental observations. [Hybrid Computational/Experimental]

Entanglement in Topological States of Matter Protected by Point Group Symmetries

Faculty Advisor: Taylor L Hughes

This theoretical project will explore properties of quantum entanglement in topological insulators and superconductors protected by spatial symmetries. We will consider the entanglement entropy and entanglement spectra and use these quantum informational measures to characterize these novel states of matter. We will learn some of the basic theory of topological insulators and entanglement and perform analytic and numerical calculations to study these systems. This project requires one semester of quantum mechanics and will involve numerical calculations so an ability to program in either Mathematica, MATLAB, C/C++, or FORTRAN is necessary to participate.

Nano-electronic devices

Faculty Advisor: Nadya Mason

The project involves fabricating and measuring nanostructures such as semiconductor nanowires and layered two-dimensional platelets. These materials are useful for the next generation of nano-electronic devices. The student will use a new nano-manipulator system to control the placement and configuration of nanowires and nano-plates. The student will study the devices using advanced tools such as atomic force microscopy and scanning electron microscopy. In addition, the student will work with a graduate student to perform electrical transport measurements on these devices. [Experimental]