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2019 NCN-URE Research Opportunities

 

NCN URE Project Descriptions

High-throughput DFT calculations for materials discovery

Faculty Advisor: Alejandro Strachan


Alejandro Strachan

Professor Alejandro Strachan
Materials Engineering

Project Description: We will build on recent work on Jupyter notebooks in nanoHUB for high-throughput DFT calculations to fill gaps of knowledge for important materials classes. One example is to compute formation energies of point defects in new high-entropy alloys. These defects are important to understand the thermodynamics of these alloys and processes like oxidation.

Suggested Majors:

  • Materials Science
  • Physics
  • Chemistry
Strachan group application areas

Required Skills: 

  • Basic programming skills
  • College-level physics
  • Quantum mechanics

Additional Desired Skills:

  • Python
  • Statistical mechanics

Videos by students who have worked with Prof. Strachan:

3 min Research Talk: Structural Analysis for Molecular Dynamics Trajectories

Thermoelectric Properties from Ab Initio Calculations: From DTFMatProp to LanTraP

Electronic and Thermoelectric Characterization of Materials from Ab Initio Calculations

 

Deploying DMFT tools in nanoHUB

Faculty Advisor: Alejandro Strachan


Alejandro Strachan

Professor Alejandro Strachan
Materials Engineering

Project Description: Dynamic mean field theory (DMFT) provides an accurate description of the electronic structure of materials where DFT fails, like in correlated electron materials. The student will learn DMFT and deploy a simple tool in nanoHUB.

Suggested Majors:

  • Materials Science
  • Physics
  • Chemistry
Strachan group application areas

Required Skills: 

  • Basic programming skills
  • College-level physics
  • Quantum mechanics

Additional Desired Skills:

  • Python
  • Statistical mechanics

Videos by students who have worked with Prof. Strachan:

3 min Research Talk: Structural Analysis for Molecular Dynamics Trajectories

Thermoelectric Properties from Ab Initio Calculations: From DTFMatProp to LanTraP

Electronic and Thermoelectric Characterization of Materials from Ab Initio Calculations


Food & energy farms – optical modeling

Faculty Advisor:

Peter Bermel

Professor Peter Bermel
Electrical and Computer Engineering

Project Description: One of the greatest global challenges today is to meet the food, energy, and water (FEW) needs of a growing population using existing resources. The challenge increases further once one eliminates unsustainable practices, and considers current land use constraints. For instance, typical solar energy installation casts deep shadows on the ground below, preventing many crops from growing nearby. There is an urgent need to develop new solutions to the global sustainability. In this project, we will aim to develop strategies to use locally collected sunlight to meet food, energy, and water needs. These strategies will entail an understanding of the best ways to design optics for these systems to allow simultaneous co-production; recognition of the requirements of modern agriculture, particularly in the Midwest; and discussions with affected stakeholders to identify and address potential concerns.

In our nanoHUB NCN research, we will construct a GUI-based simulation tool to analyze the energy production and shadowing of solar farms. Our tool will be based on optical simulations built by our group, leveraging prior open source code developed by PVlib, 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.


Image: Conceptual illustration of a strategy to divide solar spectrum into resources to support renewable energy production, agricultural crop production, and clean water purification. Adapted from Gencer et al., Scientific Reports (2017).

Suggested Majors: 

  • Electrical Engineering
  • Physics
  • Computer Engineering

Required Skills:

  • Familiarity with introductory electromagnetism and the basics of scientific computing

Additional Desired Skills: 

  • The ability and inclination to quickly learn a new scientific topic is desired.
  • Basic, working knowledge of optics is a big plus.
  • For coding, Python, C/C++, and MATLAB/Octave are our preferred languages.
  • Working familiarity with Linux and shell scripts is also a plus.

Video by student who worked with Prof. Bermel in 2018:

3 min Research Talk: Predicting and Optimizing Solar Cell Performance with Material/Surface Characteristics

 

Enhancing thermally-driven photovoltaics

Faculty Advisor: Peter Bermel

Peter Bermel

Professor Peter Bermel
Electrical and Computer Engineering

Project Description: Waste heat is an abundant resource, recoverable from the environment via the emerging technology of thermophotovoltaics (TPV). In TPV, a hot emitter illuminates a low-bandgap photovoltaic cell to generate electricity. However, heating of the latter can reduce performance, and thus requires new techniques to reduce excess heating and improve cooling of the latter.

In our nanoHUB NCN research, we will construct a GUI-based simulation tool to capture the details of the optical, thermal, and electrical processes driving TPV. We will use this to investigate new types of thermal emitters and PV cooling technologies for improved performance, and make this available for use by a broad audience. Our tool will be based on prior code developed 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.>


Image: Concept for selectively enhancing thermal radiation using an engineered emitter and filter to power a photovoltaic cell on the opposite side. Adapted from Z. Zhou et al., Energy Conversion & Management 97 (2015), pp. 63-69.

Suggested Majors: 

  • Electrical Engineering
  • Physics
  • Computer Engineering

Required Skills:

  • Familiarity with introductory mechanics, electromagnetism and the basics of scientific computing

Additional Desired Skills: 

  • The ability and inclination to quickly learn a new scientific topic is desired.
  • Basic, working knowledge of optics is a big plus.
  • Knowledge of the drift-diffusion and heat diffusion equations for current and heat transport is very helpful.
  • For coding, Python, C/C++, and MATLAB/Octave are our preferred languages.
  • Finally, working familiarity with Linux and shell scripts is also a plus.

Video by student who worked with Prof. Bermel in 2018:

3 min Research Talk: Predicting and Optimizing Solar Cell Performance with Material/Surface Characteristics


Jupyter notebook for machine learning in materials science

Faculty Advisor: Peilin Liao

Peilin Liao

Professor Peilin Liao
Materials Engineering

Project Description: Machine learning in materials science has been an active area of research for accelerating materials design and discovery. It has significant impact in how future scientists and engineers analyze data and design experiments. The goal of this project is to develop Jupyter notebooks that demonstrate the concepts and application of machine learning in materials science for both students and researchers.

Strachan group application areas

Suggested Majors: 

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

Required Skills: 

  • College-level general chemistry and physics
  • Programming course
  • Introductory course to Machine Learning/Data Science/Artificial Intelligence

Additional Desired Skills: 

  • Python

Video by student who worked with Prof. Liao in 2018:

3 min. Research Talk: Computational Catalysis - Creating a User-Friendly Tool for Research and Education


Using all of the data in materials and chemical machine learning applications

Faculty Advisor: Brett Savoie

Brett Savoie

Professor Brett Savoie,
Chemical Engineering

Project Description: 

Our group is developing new machine learning (ML) strategies that use a mixture of data from experiments and simulations to discover new materials. In existing strategies, the way chemistry is represented in the ML model plays a large role in the quality of predictions. In this project, students will learn to train and help build ML models that use new molecular representations capable of making robust predictions. In turn, these models will play a critical role in predicting the performance of new materials for applications in batteries and fuel cells.

 

 


Image: Depiction of the chemical latent space modeled by machine learning on hundreds of thousands of chemical species.

Suggested Majors: 

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

Required Skills:

  • Python and shell programming

Additional Desired Skills:

  • Statistical learning fundamentals from basic courses in statistics, probability, and introductory machine

Poster by student who worked with Prof. Savoie in 2018:

https://nanohub.org/resources/28774


Fabrication of particulate products: a computational approach to manufacturing (This project is appropriate for lower division and community college students)

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 first goal of the project is 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. The second goal of the project is to calibrate and validate the predictions of the nanoHUB tool using experimental data obtained with a bench top tablet press.



Image: Fabrication of particulate products: a computational approach to manufacturing.

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

Videos by students who worked with Prof. Gonzalez in 2018:

3 min Research Talk: Analyzing Tensile Strength and Fracture Behavior in MCC and Lactose Composite Tablets

3 min Research Talk: Investigating Deformation of Single Particles Using Micro Compression Tester


Deep learning based material by design (This project is appropriate for lower division and community college students)

Faculty Advisor: Guang Lin

Guang Lin

Professor Guang Lin
Mechanical Engineering and Mathematics

Project Description: In the research field of engineering, material design plays a crucial role. During the summer study, the students will investigate a number of deep machine learning algorithms to learn how to build effective surrogate models to predict the material performance given the datasets. Then apply the constructed deep-learning based surrogate model to do material design. We expect to develop a deep-learning based software to design the optimal material given the desired material performance requirement. The goal is (1) Develop a deep-learning based model to predict material performance; (2) develop a deep-learning based software to design the optimal material. This work has wide different applications in material manufacturing and design.

This project includes both theory and implementation in machine learning software development. The student will learn the concepts and applications of deep learning, optimization, and material design.


Fig. 1. Material by Design Sketch illustrating the range of mesoscale phenomena and their connection to molecular, mesoscale and continuum-based description in material design.

Suggested Majors:

  • Engineering
  • Computer Science
  • Mathematics and Statistics

Required Skills:

  • The student working on this project will be expected to have finished one semester of calculus and one programming course.

Additional Desired Skills: 

  • The student should be familiar with Python and some deep learning modules, such as Tensorflow, and familiar with hyper-parameter tuning to build a regression model that maps the inputs to the outputs.

Video by student who worked with Prof. Lin in 2018:

3 min Research Talk: Deep Machine Learning for Machine Performance & Damage Prediction


Phase transforming cellular materials (This project is appropriate for lower division and community college students)

Faculty Advisor: Pablo D. Zavattieri

Pablo D. Zavattieri

Professor Pablo D. Zavattieri
Civil Engineering

Project Description: Phase transforming cellular materials (PXCMs) are a family of novel architectured metamaterials which can dissipate energy elastically in response to large reversible strains. This new type of materials could be utilized in many applications including, but not limited to, buildings, automobiles, and protective gear. The PXCMs building blocks consist of a snapping mechanism that can be designed to have multiple stable configurations. Each stable configuration is associated with a unique material phase. Under large strains, the progressive phase transformation of each unit cell in a PXCM results in energy dissipation.

Based on our initial design, fabrication and numerical analysis, we developed a simulation tool on nanoHub that can predict the mechanical behavior of PXCMs based on simple geometrical parameters and material properties. Now we would like to extend this nanoHUB tool (or create a new one) to predict the behavior of PXCMs under external stimuli such as temperature, moisture, and/or electric current. Our goal is to create a simulation tool that can design and predict the response of the PXCMs under these stimuli, and ultimately predict temperature-induced and stress-induced phase transformation.

Strachan group application areas

Suggested Majors:

  • Computer Science
  • Mathematics
  • Mechanical Engineering
  • Aerospace and Aeronautical Engineering
  • Civil Engineering
Strachan group application areas

Required Skills:

  • Programming in MATLAB, Python, or C
  • Knowledge of mechanics of materials and structures

Videos by students who worked with Prof. Zavattieri in 2018:

3 min. Research Talk: Phase Transforming Cellular Materials

3 min Research Talk: Curved Beam Phase Transforming Cellular Materials (PXCMs)

3 min. Research Talk: Phase Transforming Cellular Materials Simulator