Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (2nd offering)
30 Apr 2020 | | Contributor(s):: Michael N Sakano, Alejandro Strachan
This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...
Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (1st offering)
29 Apr 2020 | | Contributor(s):: Michael N Sakano, Alejandro Strachan
Hands-on Sequential Learning and Design of Experiments
29 Apr 2020 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial introduces the concept of sequential learning and information acquisition functions and how these algorithms can help reduce the number of experiments required to find an optimal candidate. A hands-on approach is presented to optimize the ionic conductivity of ceramic...
3 min. Research Talk: Identifying the Dimensionality of Crystal Structures
12 Feb 2020 | | Contributor(s):: Franco Vera
Today, researchers worldwide have identified over 100,000 distinct bulk materials. The underlying dimensionality of these materials is not always clear however, and as such researchers have sought to identify stable, lower dimensional materials derived from the bulk parent structures. A team of...
3 min Research Talk: Hierarchical Material Optimization using Neural Networks
29 Oct 2019 | | Contributor(s):: Miguel Arcilla Cuaycong
In this presentation, we sought to use a neural network (NN) to identify optimal arrangements of four different constituents in a tape spring to be used as snapping mechanisms in phase transforming cellular material that can dissipate energy.
3 min. Research Talk: The Agrivoltaic Simulation tool
23 Oct 2019 | | Contributor(s):: Hans Torsina
The Agrivoltaic Simulation tool will calculate based on the solar panel parameters, geometries, patterns, and tracking system to provide outputs of contour shadowmaps, solar and electrical power output plots, along with input-output tables.
3 min Research Talk: Web-based Machine Learning Tool for Material Discovery and Property Prediction
26 Sep 2019 | | Contributor(s):: Bryan Arciniega
This model allows the end-user to increase their knowledge on a scarce data set by using a data-rich property set. We also investigate the effect of chemical representation and autoencoder type on property prediction and compound generation.
3 min Research Talk: Plasmonic Core-Multishell Nanowires for Optical Applications
26 Sep 2019 | | Contributor(s):: Raheem Carless
ED lights and technology are being used more often in today’s society. Compared to traditional illumination they are far more reliable and efficient, in the sense that they last longer, are environmentally friendly, and most importantly, they reduce energy waste.
3 min Research Talk: Using Machine Learning for Materials Discovery and Property Prediction
26 Sep 2019 | | Contributor(s):: Mackinzie S Farnell
Machine Learning models present a transformative method of optimization and prediction in science and engineering research. In the chemical sciences, unsupervised deep learning models such as autoencoders have shown to be useful for property prediction and material...
NCN Undergraduate Research Experience 2019 - 3 Minute Research Talks
25 Sep 2019 |
As part of each student's undergraduate research experience, each student gave a 3 minute presentation describing their research work.
3 min. Research Talk: Image Analysis of a Vesicle to Calculate the Bending Modulus
04 Feb 2019 | | Contributor(s):: Pheobe Jane Appel
3 min. Research Talk: Phase Transforming Cellular Materials
04 Feb 2019 | | Contributor(s):: Valeria Grillo
3 min. Research Talk: The Exciton Spectra Simulator of Photosynthetic Protein-pigment Complex
04 Feb 2019 | | Contributor(s):: Qifeng Chen
NCN Undergraduate Research Experience 2018 - 3 Minute Research Talks
04 Feb 2019 |
3 min Research Talk: Deep Machine Learning for Machine Performance & Damage Prediction
04 Feb 2019 | | Contributor(s):: Elijah Reber
In this talk, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether...
3 min Research Talk: Grain Boundary Motion Analysis
04 Feb 2019 | | Contributor(s):: Jeremy Seiji Marquardt
This talk aims to present the results of grain boundary motion simulations through a generalized program that streamlines and optimizes the analysis process. Various simulations examining the effects of grain boundary energy and mobility were run through Idaho National Laboratory's...
3 min Research Talk: Ellipsoidal Particle Detection
31 Jan 2019 | | Contributor(s):: Gabrielle Kershaw
The objective of this project was to create code using functions found in MATLAB Image Processing Toolbox, such as edge detection, thresholding, and morphology to isolate the desired ellipsoidal particles in images obtained from experiments. The code was to become a research tool that...
3 min Research Talk: Analysis of Radiation Induced Segregation in Fe-Cr-Al Alloys
31 Jan 2019 | | Contributor(s):: Timothy Joe Pownell
This presentation gives an overview of the results and tool that were developed from data on Radiation Induced Segregation of the prospective cladding material Fe-Cr-Al.
3 min Research Talk: GUI for the Surface Evolver – Polycrystalline Grain Growth
31 Jan 2019 | | Contributor(s):: Kevin K Ngo
This talk summarizes how the the online simulation tool, GUI for the Surface Evolver, was created as well as how it will impact research in polycrystalline grain growth. Polycrystalline grains have surface energies and tensions associated with them that cause grain boundaries to move (grow).
3 min Research Talk: AFM And EBSD Cross-Comparison Analysis Tool
31 Jan 2019 | | Contributor(s):: Andrew Martin Krawec
This talk describes an approach to analyzing the crystal structure using data collected from AFM and EBSD scans to build an accurate image of the crystal structure and orientation in the ceramic