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2018 NCN-URE (SURF) Research Opportunities

NCN has several 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. 


NCN URE Project Finder

Project Aerospace Biological Engineering Chem E Chemistry Civil Computer Science / Engineering Electrical Materials Science Physics Mathematics (Applied) Mechanical Nuclear
Bermel- SC           CS ECE MSE Physics      
Blendell AAE   ChemE       ECE MSE Physics   ME NE
Gonzalez     ChemE   Civil CS   MSE     ME  
Koslowski             ECE MSE     ME  
Liao     ChemE Chem   CS ECE MSE Physics   ME  
Lin AAE     BioE ChemE   Civil CS ECE MSE   Math ME NE
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  
Zavattieri AAE       Civil           ME  

 

NCN URE Project Descriptions

 


Jupyter workflows for multiscale modeling of materials


Alejandro Strachan

Professor Alejandro Strachan
Materials Engineering

Strachan group application areas

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.

Suggested Majors:

  • Materials Science
  • Physics
  • Chemistry

Required Skills: 

  • Basic programming skills, Python would be a plus
  • College-level physics
  • Quantum mechanics, statistical mechanics would be a plus

Computational modeling of heterogeneous catalysis on bimetallic catalysts

Faculty Advisor: Peilin Liao

Peilin Liao

Professor Peilin Liao
Materials Engineering

Project Description: 

Strachan group application areas

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.

Suggested Majors: 

  • Materials Science
  • Chemical Engineering
  • Chemistry
  • Physics
  • Electrical Engineering
  • Mechanical Engineering
  • Computer Science

Required Skills: 

  • College-level general chemistry and physics
  • Undergraduate-level quantum chemistry or quantum physics
  • Programming course

Additional Desired Skills: 

  • Python

Deep machine learning on predicting material damage

Guang Lin

Professor Guang Lin
Mechanical Engineering and Mathematics

Faculty Advisor: Guang Lin

 
Strachan group application areas

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.

Suggested Majors:

  • Engineering
  • Computer Science
  • Mathematics and Statistics

Required Skills:

  • Programming in Python, Matlab or C++
  • Calculus
  • Linear algebra
  • Computer programming
  • Basic probability
  • Statistics

Additional Desired Skills: 

  • Numerical methods
  • Bayesian probability
  • Optimization

Excitation energy and electron transport models for natural and artificial photosynthetic systems and molecular crystals

Faculty Advisor: Lyudmila Slipchenko

Lyudmila Slipchenko

Professor Lyudmila Slipchenko
Theoretical Chemistry

Figure 1

Classical representation of electron-phonon coupling model

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.

Suggested Majors: 

  • Computer Science
  • Electrical and Computer Engineering

Required Skills:

  • Linear algebra
  • Experience of developing software in Python

Additional Desired Skills:

  • Quantum mechanics or related
Figure 2
Figure 3

Project 1: Modeling of solar cell performance across various device characteristics

Peter Bermel

Professor Peter Bermel
Electrical and Computer Engineering

Faculty Advisor: Peter Bermel

Strachan group application areas

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.

 

Suggested Majors: 

  • Electrical Engineering
  • Physics
  • Materials Science
  • Computer Engineering

Required Skills:

  • Semiconductor device physics
  • Introductory electromagnetism
  • Programming in Python (preferred), Matlab, or C++

Additional Desired Skills: 

  • Knowledge of photovoltaic devices
  • Scientific modeling
  • Motivation and ability to quickly learn new topics as needed is highly desirable

Development of a machine learning tool to optimize thermal transport

Xiulin Ruan

Professor Xiulin Ruan
Mechanical Engineering

Faculty Advisor: Xiulin Ruan

Project Description: I

Many heat transfer applications, such as thermoelectric energy conversion, thermal barrier coatings, and thermal management of electronics, require the optimization of thermal conductivity of the material to reach minimum or maximum. Conventionally, such optimization was done by exhausting different structures and compositions of the materials, hence it is a time consuming and even impractical task. Here, we aim to develop a machine-learning based optimization tool to minimize the thermal conductivity of a nanostructure called superlattice. By modeling a limited number of material structures and learn from the results, machine-learning will guide the design to new structures with likely better properties. The goal is to reach the same optimum design by searching only a fraction of the entire design space. We will convert an in-house code to a nanoHUB simulation tool.

More information: https://engineering.purdue.edu/NANOENERGY/

 

Suggested Majors: 

  • Mechanical Engineering
  • Physics
  • Materials Science

Required Skills:

  • Programming

Additional Desired Skills: 

  • Heat Transfer
  • Nanotechnology
  • Motivation and ability to quickly learn new topics as needed is highly desirable

Stress induced void formation in thin films

Marisol Koslowski

Professor Marisol Koslowski,
Mechanical Engineering

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.

Suggested Majors: 

  • Mechanical Engineering
  • Materials Engineering
  • Electrical Engineering.

Desired Skills:

  • Knowledge of programming with Python

Developing machine learning models of novel chemicals and materials

Faculty Advisor: Brett Savoie

Brett Savoie

Professor Brett Savoie,
Chemical Engineering

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.

Suggested Majors: 

  • Chemical Engineering
  • Computer Science
  • Chemistry
  • Physics
  • Mathematics

Required Skills:

  • Linear algebra
  • Python programming

Additional Desired Skills:

  • Experience with TensorFlow

 


Faceting phase diagrams

John Blendell

Professor John Blendell,
Materials Engineering

John Blendell

Professor Edwin Garcia,
Materials Engineering

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.

Suggested Majors: 

  • Materials Engineering
  • Physics
  • Engineering

Required Skills:

  • Calculus
  • Physics
  • Ability to build a GUI

Additional Desired Skills:

  • Basic Materials Science course

Image analysis of vesicle membranes

Alexander V. Kildishev

Professor Vivek Narsimhan,
Chemical Engineering

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.

Suggested Majors: 

  • Chemical Engineering
  • Biological Engineering
  • Physics
  • Chemistry
  • Applied Mathematics

Required Skills:

  • Basic programming skills, MATLAB or Python
  • College-level physics, particularly statistical mechanics

Additional Desired Skills:

  • College level mathematics, Fourier series/transforms

Development of a nanoHUB tool for biologically inspired
fibrous material systems using LAMMPS

Pablo D. Zavattieri

Professor Pablo D. Zavattieri
Civil Engineering

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.

Suggested Majors:

  • Civil Engineering
  • Mechanical Engineering
  • Aerospace Engineering

Required Skills:

  • Basic physics
  • Python and/or C programming
  • Basic mechanics of materials

Additional Desired Skills: 

  • Numerical methods
  • Molecular dynamics (MD)

Fabrication of particulate products: a computational approach to manufacturing

Faculty Advisor: Marcial Gonzalez

Marcial Gonzalez

Professor Marcial Gonzalez
Mechanical Engineering

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

 

Suggested Majors:

  • Mechanical Engineering
  • Civil Engineering
  • Materials Science Engineering
  • Chemical Engineering
  • Computer Science

Required Skills:

  • Basic understanding of mechanical properties of solid materials:
    • Roy R. Craig Jr., Mechanics of Materials, Chapter 2 -- especially the parts on stress-strain curves
    • The same content is also in many MSE textbooks, such as Callister
    • There is also a lecture on nanoHUB that covers the content at a more advanced level: https://nanohub.org/resources/6052
  • Basic coding experience in MATLAB and willingness to learn

Additional Desired Skills: 

  • Completion of any science/engineering course that had a laboratory component
  • Familiarity with the current research activities taking place at Purdue’ Center for Particulate Products and Processes (CP3): https://engineering.purdue.edu/CP3

Developing physics-based compact models for novel Terahertz (THz) devices using ultrafast magnetic phenomena

Faculty Advisor: Pramey Upadhyaya

Pramey Upadhyaya

Professor Pramey Upadhyaya
Electrical and Computer Engineering

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)]

Suggested Majors:

  • Electrical Engineering
  • Physics
  • Mathematics
  • Materials Science/Mechanical Engineering
  • Computer Engineering

Required Skills:

  • Undergraduate level understanding of Classical Physics
  • Electrical circuits
  • Mathematical understanding of differential equations
  • Calculus

Additional Desired Skills: 

  • Knowledge of magnetism, modeling experience in MATLAB and spice-like circuit simulators is a plus.
  • Motivation and ability to quickly learn new scientific topics is highly desirable
    .