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NCN has over 20 projects available for NCN-SURF students in Summer 2018. Each project has its own requirements, and is appropriate for students in different majors.
You can find projects that match your major in the table below, or browse through the full list of projects that follows.
Once you have found projects you are interested in, follow the directions on the SURF application page and select "Network for Computational Nanotechnology (NCN) / nanoHUB" as one of your top choices. Indicate your specific project preference and qualifications in your SURF application, in the text box for Essay #2.
Project | Aerospace | Biological Engineering | Chem E | Chemistry | Civil | Computer Science / Engineering | Electrical | Materials Science | Physics | Mathematics (Applied) | Mechanical | Nuclear |
Ardekani | ChemE | Physics | Math | ME | ||||||||
Bermel- QE | CS | ECE | Physics | |||||||||
Bermel- SC | CS | ECE | MSE | Physics | ||||||||
Blendell | AAE | ChemE | ECE | MSE | Physics | ME | NE | |||||
Christov | ChemE | Physics | Math | ME | ||||||||
El-Azab- CDD | AAE | MSE | Physics | ME | ||||||||
El-Azab- DDD | AAE | MSE | Physics | ME | ||||||||
Gonzalez | ChemE | Civil | CS | MSE | ME | |||||||
Kildishev | CS | ECE | Math | |||||||||
Koslowski | ECE | MSE | ME | |||||||||
Liao | ChemE | Chem | CS | ECE | MSE | Physics | ME | |||||
Lin | AAE | BioE | ChemE | Civil | CS | ECE | MSE | Math | ME | NE | ||
Mukherjee | ChemE | Chem | MSE | Physics | Math | ME | ||||||
Narsimhan | BioE | ChemE | Chem | Physics | Math | |||||||
Ruan | ECE | MSE | Physics | ME | ||||||||
Savoie | ChemE | Chem | CS | Physics | Math | |||||||
Slipchenko | CS | ECE | ||||||||||
Strachan | Chem | MSE | Physics | |||||||||
Upadhyaya | CS | ECE | MSE | Physics | Math | ME | ||||||
Wharry | CS | MSE | ME | NE | ||||||||
Zavattieri | AAE | Civil | ME |
Faculty Advisor: Alejandro Strachan
Project Description: The goal of this project is to create workflows using Jupyter notebooks that combine a variety of online repositories, nanoHUB simulations, and visualization tools to demonstrate multiscale materials modeling with a combination of density functional theory, molecular dynamics and continuum simulations.
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Faculty Advisor: Arezoo Ardekani
Project Description: This project involves developing a Stokes flow solver to analyze the motion of rigid particles and swimming microorganisms of simple shapes (spherical, spheroidal or cylindrical) near planar/spherical interfaces. For this purpose, the student makes use of either Regularized Stokeslet method. Starting with the solution of flow field due to these particles in unbounded fluids, they utilize the method of images technique to study their motion near surfaces.
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Faculty Advisor: Janelle Wharry
Project Description: Students will develop a tool to extract true stress-true strain relationships from miniature mechanical tests. Miniature mechanical testing techniques are increasingly being utilized to assess the strength of nanomaterials, including thin films, ion irradiated or implanted layers, and other volume-limited specimens. These techniques measure load-displacement curves while simultaneously collecting video of the test specimen, from which engineering stress-strain curves are generated.
However, researchers often desire true stress-strain curves to gain further insight into deformation mechanics. Students will develop an algorithm to track the real-time evolution of specimen dimensions. They will then couple this algorithm with the load-displacement curve from the mechanical test, to generate a true stress-strain relationship from each test. This deliverable will not only help researchers better understand mechanics of materials, but it will also tremendously accelerate and automate the analysis of miniature mechanical tests.
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Faculty Advisor: Peilin Liao
Project Description:
We are interested in developing catalysts for use in solar cells, to split water to form hydrogen and oxygen gas. This process converts solar energy to chemical energy. However, slow kinetics in the hydrogen evolution reaction limits the efficiency of the current transition-metal catalysts.
First-principles computational methods, such as density functional theory (DFT), have been applied to optimize geometry of the initial reactant and final product states and locate transition states for evaluating reaction barriers. DFT calculations provide detailed atomistic and electronic structures of catalytic surfaces and reactive intermediates. Analysis of the computational results gives valuable insights into the energetics and reaction mechanism.
In this project, the student will learn basic theory and develop a Python interface to set up structural models, run calculations, and analyze computational results.
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Faculty Advisor: Guang Lin
Project Description: Material damage is a physical discontinuity in a material. It can be introduced either during manufacturing or in the service stage. The damage can impair usefulness or normal functioning of the material. The research goal of this project is to quantitatively evaluate and predict the damage shape, size, and effect using deep machine learning tools.
In this project, we will employ a data-driven approach using deep convolution-neural- networks to characterize the evaluation of material damage based on different load conditions, crack location, shape, and size. This work has many different applications across many disciplines, such as predicting bridge and road damage and crack propagation in wind turbines.
The student working on this project will be expected to familiarize with state-of- the-art deep learning modules, such as tensorflow, as well as with network architecture and hyperparameters to build a regression that maps the inputs to the outputs.
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Faculty Advisor: Lyudmila Slipchenko
Project Description: Excitation energy transfer and related electron transport are universal phenomena governing photosynthesis in plants and bacteria, and exploited by humankind in photovoltaic devices and FRET spectroscopy. Thus, predictive modeling of the energy and electron transport and electron-phonon interactions is essential for advancing our fundamental knowledge and technological progress.
The Slipchenko group develops energy and electron transport models that describe electron-phonon couplings in multi-chromophore systems. So far these models have been applied to understand vibronic interactions in gas-phase multi-chromophores for which accurate experimental data are available. Currently we work on extending these models and software for applications in photosynthetic organelles and molecular crystals.
The goal of the current project is to develop an interactive NanoHUB module for predicting vibronic interactions and energy/electron transport in molecular aggregates. The developed module can be used by theoretical and experimental groups working in the fields of solid state physics, biophysics and physical chemistry and adapted as a teaching tool in quantum mechanics, spectroscopy and nanotechnology courses.
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Faculty Advisor: Partha P. Mukherjee
Project Description: Lithium-ion batteries are the energy storage technology of choice for electric vehicles. Operational characteristics, including performance, life and safety, of such batteries, however, depend on the electrode microstructural complexities and associated stochasticity. The electrode microstructure is a strong function of the multi-phase composition (i.e., weight ratio of different solid phases) and processing conditions. X-Ray tomography is a non-invasive tool which can provide 3D microstructural information of porous electrodes. However, extracting interfacial and multi-phase stochasticity attributes continues to remain challenging.
In this project, a data-driven model will be developed from a discrete set of tomographic data. In addition to correlating electrode microstructures with composition and calendaring pressure, this model will predict effective microstructural and transport properties, such as active area, tortuosity and conductivity. The ultimate goal is to understand the role of microstructural stochasticity on the electrode properties and performance.
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Faculty Advisor: Peter Bermel
Contour map of open circuit voltage for a heterojunction solar cell as a function of its material properties, specifically, the logarithm of Shockley-Read-Hall lifetime (x-axis), and the logarithm of the surface recombination velocity (y-axis).
Project Description: Photovoltaic module design has been rethought to increase efficiency and thus take greater advantage of the abundant solar energy available to us. We recently made advancements in explaining the physical mechanisms behind observed record efficiency in one of these designs with a physics-based model and simulation work with our nanoHUB tool – ContourPV – developed by a former NCN intern.
This tool develops contour plots of common device metrics for this specific structure across a range of values for two physical parameters. However, functionality is limited. We seek to expand upon this tool to allow the user to investigate multiple parameters of interest for a variety of structures and materials systems. This will let researchers optimize materials and devices without experimental work and determine possible physical parameters for a device given multiple unknowns, some of which cannot be easily experimentally determined.
For this project, the student will create a more universal tool for sweeping multiple device parameters for different devices and materials systems, allowing researchers across the photovoltaic community to further utilize highly accurate 1D solar device modeling.
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Faculty Advisor: Peter Bermel
Project Description: Quantum photonics is a tremendously promising field likely to revolutionize secure communications and information processing. Deterministic single photon sources are an integral part of these emerging technologies, and several strategies have emerged for creating such sources. Our recent investigations have focused on color center impurities in crystals, which offer the potential of achieving relatively long lifetimes even at room temperature. Through photonic design and basic quantum analysis, it is possible to greatly improve on existing sources, which could benefit the field as a whole.
In our nanoHUB SURF research, we will construct a GUI-based simulation tool to analyze quantum emitter structures. The student will primarily be focused on predicting the emission and absorption spectra of nano-diamonds containing various impurity atoms. Of interest are the spectra of xenon, boron, nitrogen, and other single impurity atoms and vacancies within a nano-diamond. The student will also investigate the enhancement of these spectra by considering the effects of several configurations of coatings surrounding the nano-diamond. Our tool will based on finite-difference or finite-element time domain electromagnetic simulations built by our group, and will be hosted and run through nanoHUB.org - an open-access science gateway for cloud-based simulation tools and resources in nanoscale science and technology.
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Faculty Advisor: Xiulin Ruan
Project Description: In this project, the undergraduate student will assist a PhD student to deploy a tool for simulating heat transfer across metal-dielectric interface. The conventional two-temperature equations for electron-phonon coupled thermal transport across metal/nonmetal interfaces have been modified to include the possible coupling between metal electrons with substrate phonons. Our previous two-temperature molecular dynamics (TTMD) approach has been extended to solve these equations numerically at the atomic scale, and the method is demonstrated using Cu/Si interface as an example. Based on the temperature profiles from TT-MD, a new mixed series-parallel thermal circuit can be constructed. This simulation tool will also be deployed to nanoHUB.
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Faculty Advisor: Marisol Koslowski
Project Description: Common failure modes for Cu interconnects include electromigration, stress-induced voiding, fracture, and delamination between materials. As downscaling continues, reliability issues related to stress migration increase due to thermal stresses that arise during manufacturing. Stress gradients drive the atoms from compressive regions towards tensile regions resulting in void nucleation and growth. Finite element simulations that couple thermal stresses, plastic deformation and grain boundary diffusion will be performed to investigate the effect of texture and grain size distribution on residual stresses built up during manufacturing of interconnects.
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Faculty Advisor: Anter El-Azab
Project Description: The aim of the project is to develop a tool for nanoHub to analyze the cross-slip rates of dislocations in metallic crystals under the condition of plastic deformation. The tool will use the data obtained from Discrete Dislocation Dynamics (DDD) in the form of time series, as input and then perform a sequence of statistical analysis on it. The statistical analysis involves correlation time estimation, temporal coarse graining, smoothing and generation of probability density distribution. This analysis will help us to extract a meaningful information from the time series of cross-slip rates and enrich our understanding of this important phenomenon.
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Faculty Advisor: Anter El-Azab
Project Description: The mechanics of dislocation motion is essential to understanding plastic deformation of materials at mesoscale. When simulating the interactions of dislocation, an abundance of information is created. In order to get an understanding of the results we have to employ statistical methods to interpret the results. A set of Matlab codes will be developed to help in the statistical analysis of large data sets produced by the continuum dislocation dynamics simulations. These tools will help to analyze the complex internal structures of the elastic strain and lattice rotations created by interacting dislocations. Due to the complexity of the resulting structures, spatial statistical methods will be employed to aid in the understanding of dislocation-induced microstructures and provide a quantitative way to compare numerical results to experimental measurements.
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Faculty Advisor: Brett Savoie
Project Description: Developing new chemicals and materials is critical to advancing energy technology, but this process is frustrated by the cost and time constraints of experimental synthesis and characterization. The goal of this project is to utilize a growing database of computational characterizations on organic liquids and polymers developed by the Savoie group and apply modern machine learning algorithms for predicting the properties of novel chemicals and materials.
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Faculty Advisor: John Blendell and Edwin Garcia
Project Description: On area of research in ceramic processing is the relationship between interfacial energy anisotropy and microstructure evolution. As many properties are impacted by changes in microstructure, an understanding of the effect of composition and processing on interfacial energy is needed.
The interfacial energy anisotropy is reflected in the faceted shapes of pores and surface. Quantitative measurements of the faceting can be made with AFM measurements, however a link to the orientation of the specific surface is needed to develop a faceting phase diagram in orientation space that can be used to analyze microstructure evolution. For single crystals this is straightforward, but slow. Measurements on polycrystalline samples provides a rapid method of examining many orientations. Developing a easy to use tool that allows for the correlation of the two measurements is needed to make this technique practical.
For an early attempt at this see: VIS '96 Proceedings of the 7th conference on Visualization '96 Pages 397-ff.
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Faculty Advisor: Alexander V. Kildishev
Project Description: In this project student will work in our simulation team that develops modeling tools to simulate light propagation in complicated nanostructured engineered optical materials and devices. Our team goal is to drive, validate and support experimental studies in nanophotonics group at Birck Nanotechnology Center with simulations and theoretical studies.
While most of the time we work on developing analytical and numerical methods in computational nanophotonics, and performing experiment-fitted simulations, we also are working on delivering our software tools to nanophotonics community using nanoHUB platform.
So far, our Simulation Toolkit at nanoHUB consists of 11 simulation tools tightly connected to our recent research in nanophotonics, including light propagation in nanostructured metamaterials, simulation of cylindrical “optical black hole” made of metamaterials, nanolasers, tunable graphene-based devices, etc. In Figure 1, we show some selected representative structures and simulation results.
Student will be involved in the development of one or more simulation tools that are of particular interest and need in the moment:
Depending on skills and background, the student can be involved in the development and implementation of the numerical models, simulations connected to the current experiments, development of simulation tools at nanoHUB.
Figure 1. Left to right: (a) SEM image of nanoantennas, for optimal design simulations of the antennas are required; (b) simulation studies of “optical black hole” made of metamaterials; (c) schematics of a graphene-based tunable device, where the optical properties of graphene are controlled by the applied bias voltage.
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Faculty Advisor: Vivek Narsimhan
Project Description: Vesicles are elastic and highly deformable sacs of fluid enclosed by a lipid bilayer. These entities are critical for the intracellular compartmentation and molecular trafficking that underlie the signaling, defense and nutrition vital for an organism’s survival. Similar lipid architectures are also used in industrial applications ranging from drug and gene delivery to fabric softeners. Lastly, vesicles are model systems to understand fundamental processes that occur in all cellular membranes (e.g., budding, fusion, membrane-protein interactions). For these reasons, there is immense interest to characterize the physical properties and mechanical behavior of vesicular systems under various conditions.
In this project, the student will develop and publish image processing codes to analyze microscope images of vesicles. The goal of these codes is to extract elastic properties of the lipid bilayers through thermal fluctuations of the vesicle shape over time. Ambitious students will also have the opportunity to synthesize vesicles in lab, examine more complicated membrane architectures (multicomponent vesicles), and solve equations describing the shape dynamic of these entities under weak flow.
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Faculty Advisor: Pablo D. Zavattieri
Project Description: Nature has shown remarkable, efficient and elegant solution for developing high-performance materials for extreme conditions using fibers. In fact, most biological materials have evolved extremely efficient fibrous architectures that are both strong and tough, two properties that are typically mutually exclusive in engineering materials. Proteins are essential building blocks of life, forming a diverse group of biological materials. These materials represent the merger of structure and material, through hierarchical formation of structural elements that range from the nanoscale to the macroscale. To study such as complex materials, different tools like molecular dynamics and coarse-grained models have been employed. The student enrolled in this project will help develop a NanoHUB tool for fibrous materials systems (like the one depicted in the Figure below) using in LAMMPS. Fibers will be model as an arrange of “molecules” or “beads” using a coarse-grained modeling approach where the interaction between beads will be determined by the mechanical properties of the fibers.
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Faculty Advisor: Ivan C. Christov
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This project is aimed at developing a nanoHUB tool to simulate kink dynamics. In materials science, kinks are topological defects between two stable states in a material, where the crystal lattice locally adjusts from one stable arrangement to another. Exposing the material to external forcing in the form of, say, electric or magnetic fields can cause a phase transition, where a configuration becomes unstable and the atoms must rearrange. Kinks can nucleate, annihilate, and translate through a material, and knowledge of kink dynamics can be used to create one-dimensional models of phase transitions.
In the simulation tool, the user will be able to input various simulation parameters (such as time step, lattice spacing, total run time) and the model potential of the field theory (such as a polynomial or non-polynomial function). The Langevin simulation will be performed in real time, showing animations of the equilibration (or lack thereof) of coherent structures. The simulation tool will also build up a probability distribution function (PDF) of accessed states on-the-fly. Options will be provided to compare the computed PDF to analytical results for selected potentials.
Although this work is fundamental in nature, understanding, calibrating and simulating kink dynamics at phase transitions has implications for designing novel materials. For example, phase transitions in ferroelectric materials dynamically tunes their capacitance and phase transitions in ferroelastic materials can be used to create piezoelectric sensors and mechanical switches.
Figure 1 shows how the presence of noise in a system at fixed temperature can cause kinks to nucleate, annihilate and translate along a cardinal direction. We study these dynamic behaviors through large-scale stochastic simulations. Figure 2 shows the emergence of kink dynamics in the propagation of twist defects along buckled graphene ribbons.
Fig. 2 Reproduced from https://journals.aps.org/prb/abstract/
10.1103/PhysRevB.96.094306. Motion of defects in buckled graphene ribbons can be described by 1D Langevin simulations of kink dynamics.
Fig. 1. Instantaneous snapshot of a Langevin simulation of kinks (coherent structures interpolating -1 to +1 in the picture). The structures move, nucleate and annihilate dynamically in the simulation. Statistical properties are calculated in the many-kink (thermodynamic) limit.
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Faculty Advisor: Marcial Gonzalez
Project Description:
Particulate products are ubiquitous and highly valued across a range of industry sectors, as diverse as agricultural, energetic materials and pharmaceutical. These products contribute more than one trillion dollars to the U.S. economy, which is the world’s larger manufacturer in this sector. Compaction of micro- and nano-powders is a manufacturing process used in most of these industries. It consists of the synthesis of loose powder blends into solid bodies. Since the performance of particulate products is directly related to their microstructural features, the fundamental understanding of the compaction process becomes of paramount importance.
The specific goals of the project are to expand the current capabilities of the nanoHUB tool Powder Compaction (https://nanohub.org/resources/gscompaction) by:
(i) modeling compacted products of different shapes and sizes,
(ii) accounting for the elastic relaxation compacted powders experience after being plastically deformed during fabrication.
These two features are key to have a realistic computational approach to the manufacturing process, and thus address questions relevant to manufacturability and product quality
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Faculty Advisor: Pramey Upadhyaya
Project Description:
Information encoded in magnets is non-volatile. Namely, once information is written in magnetic bits, external power is not needed to keep it intact. This offers a major advantage in terms of solving the problem (faced by the present-day electronic devices) of minimizing energy wasted in preserving information. In this context, discovery of phenomena allowing for manipulating magnetic order via electrical fields, current and light provides alternative route to constructing magnet-based information processing and communication devices- giving rise to a new field dubbed spintronics.
In this project, we will develop compact circuit-like models, that can be used by circuit designers, for constructing novel spintronic devices. Our developed compact models will be based on magnetization dynamics equations, and will be hosted on the nanoHUB.org – an open-access hub of simulation tools to be used by the nanotechnology community.
Special emphasis will be on the more recently discovered phenomena of manipulation of antiferromagnets electrically (see Figure below). Due to their underlying magnetic structure, antiferromagnets promise ultra-high density, immunity to external noise, and (most importantly) manipulation in the Terahertz (THz) time scale. THz is a frequency range of high interest for biomedical, information processing, communication and military applications. However, there is a dearth of information processing and communication devices existing in this frequency range (famously known as the “THz gap”). The compact models developed here will provide a pathway to using magnetic materials to fill the THz gap.
Figure 1: (a)An antiferromagnet (AFM) can be controlled electrically by a voltage (V) or current (I) through a heavy metal (HM). We will develop compact models for this control (shown in insets). These compact models can be combined to form a THz device, such as THz oscillator [schematically shown in (b)]
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