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PolymerXtal - Polymer Crystal Structure Generator and Analysis Software
15 Dec 2020 | | Contributor(s):: Tongtong Shen, Jessica Nash, Alejandro Strachan
PolymerXtal is a software designed to build and analyze molecular-level polymer crystal structures.
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"Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic
15 Nov 2020 | | Contributor(s):: Rachel Altovar, Susan P Gentry
Expanding on the pre-existing resource on nanoHUB: “Turning Fruit Juice into Graphene Quantum Dots” this resource expands on the concepts in the experimental guide to give a comprehensive overview of materials pertaining to concepts and ideas within the...
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MODULE 3 - Structures: "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic
15 Nov 2020 | | Contributor(s):: Rachel Altovar, Susan P Gentry
In MODULE 3- Structures in the "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans, crystal structures and systems are investigated. This module relates back to graphene and how its structure relates back to its unique properties in comparison to other forms of...
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MODULE 4 - Quantum Mechanics: "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic
15 Nov 2020 | | Contributor(s):: Rachel Altovar, Susan P Gentry
The last and final module in the "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans, studies basic concepts in quantum mechanics such as quantum dots, band gap theory of solids, waves vs. particles, and the photoelectric effect. The activity for this module...
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MODULE 1 - Graphene: "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic
13 Nov 2020 | | Contributor(s):: Rachel Altovar, Susan P Gentry
The first module in "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans, explores the material, graphene, how it was discovered, and the unique properties that it has. The activity paired with this lesson plan re-creates the famous "sticky-tape"...
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MODULE 2 - Sizes: "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic
13 Nov 2020 | | Contributor(s):: Rachel Altovar, Susan P Gentry
The next installment of Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans delves into the concept of size and how materials and their properties may change at the macro-, micro-, and nanoscale. Activities include viewing images from a microscope to determine...
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Hands-on Deep Learning for Materials Science: Convolutional Networks and Variational Autoencoders
13 Nov 2020 | | Contributor(s):: Vinay Hegde, Alejandro Strachan
This tutorial introduces deep learning techniques such as convolutional neural networks and variational auto encoders from a materials standpoint.
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Machine Learning Defect Behavior in Semiconductors
10 Nov 2020 | | Contributor(s):: Arun Kumar Mannodi Kanakkithodi, Rushik Desai (editor)
Develop machine learning models to predict defect formation energies in chalcogenides
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Simulating Electronic Properties of Materials Using Ab Initio Modeling with SIESTA on nanoHUB.org
08 Oct 2020 | | Contributor(s):: Lan Li
The simulation tool featured in this presentation is MIT Atomic-Scale Modeling Toolkit.
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Module 5: Neural Networks for Regression and Classification
01 Oct 2020 | | Contributor(s):: Saaketh Desai, Alejandro Strachan
This module introduces neural networks for material science and engineering with hands-on online simulations. Neural networks are a subset of machine learning models used to learn mappings between inputs and outputs for a given dataset. Neural networks offer great flexibility and have shown...
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Module 4: Linear Regression Models
01 Oct 2020 | | Contributor(s):: Michael N Sakano, Saaketh Desai, Alejandro Strachan
This module introduces linear regression in the context of materials science and engineering. We will apply liner regression to predict materials properties and to explore correlations between materials properties via hands-on online simulations. Linear regression is a supervised machine...
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Module 7: Active Learning for Design of Experiments
30 Sep 2020 | | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum
This module introduces active learning in the context of materials discovery with hands-on online simulations. Active learning is a subset of machine learning where the information available at a given time is used to decide what areas of space to explore next. In this module, we will explore...
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MATE 370 Virtual Lab: Exploring Phase Transformations Through nanoHUB Nanomaterial Mechanics Explorer Tool
24 Sep 2020 | | Contributor(s):: Mohsen B Kivy, Crystal Ipong
This lab explores the kinetics of phase transformation using nanoHUB tools.
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MATE 370 Virtual Lab: Exploring Nucleation, Crystallization, and Growth through nanoHUB Virtual Kinetics Tools
24 Sep 2020 | | Contributor(s):: Mohsen B Kivy, Crystal Ipong
This lab explores the kinetics of nucleation, crystallization, and growth processes using nanoHUB tools.
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MATE 370 Virtual Lab: Exploring Diffusion through nanoHUB Defect- coupled and Concentration-dependent Diffusion Tools
24 Sep 2020 | | Contributor(s):: Mohsen B Kivy, Crystal Ipong
This lab explores the kinetics of solid-state diffusion using nanoHUB tools.
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Linear Regression Young's modulus
24 Sep 2020 | | Contributor(s):: Michael N Sakano, Saaketh Desai, Alejandro Strachan
Use linear regression to extract Young's modulus and yield stress from stress-strain data
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Machine Learning in Materials - Center for Advanced Energy Studies and Idaho National Laboratory
24 Sep 2020 | | Contributor(s):: Alejandro Strachan
his hands-on tutorial will introduce participants to modern tools to manage, organize, and visualize data as well as machine learning techniques to extract information from it. ...
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nanoHUB: Online Simulation and Data
24 Sep 2020 | | Contributor(s):: Alejandro Strachan
These slides introduce nanoHUB, an open platform for online simulations and collaboration.
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Materials Science Education Champion Seminar Series
07 Sep 2020 | | Contributor(s):: Susan P Gentry
This webinar series will highlight computer simulation modules that are used by MSE educators around the country. Each topical webinar will feature a presentation by MSE faculty that use the tools as well as time for group discussion.
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Computational Labs in Kinetics of Materials and Process Design (California Polytechnic State University)
07 Sep 2020 | | Contributor(s):: Mohsen B Kivy, Crystal Ipong
Kinetics of Materials and Process Design (MATE 370) is a 4-unit major course for junior-year undergraduate students of the Materials Engineering Department, Cal Poly State University. The Materials Engineering Department endorses the applications of theory to practice through its...