Materials science is the understanding and application of properties of matter. Materials science studies the connections between the structure of a material, its properties, methods of processing and performance for given applications.
Please see the nanoHUB Group Materials Science for highlighted materials science related items.
For educators please see the nanoHUB group MSE Instructional Exchange
For the latest tools that combine materials science with machine learning and data science see the nanoHUB group Data Science and Machine Learning
Oct 05 2022
Density Functional Theory: Introduction and Applications with Prof. André Schleife
Summer Research at Princeton University
26 Sep 2022 |
This presentation discusses some of the undergraduate summer research opportunities available.
RAIN Network: Scanning Electron Microscope
26 Sep 2022 | | Contributor(s):: Paul D. Asimow
Sep 13 2022
MAST-ML and Machine Learning Lab Module Workshop
CHEM 870 Tutorial 6b: Binding Energy, DFT, and CO2 Capture II
04 Sep 2022 | | Contributor(s):: Nicole Adelstein
The main goal of these activities is to calculate the binding energy of CO2 to linker molecules in metal organic frameworks (MOFs). CO2 is a greenhouse gas. One necessary component of combating climate change is removing CO2 from the atmosphere. We will use density functional theory (DFT)...
Microstructure Modeling with OOF2 and OOF3D
26 Aug 2022 | | Contributor(s):: Andrew Reid, Stephen Langer
The OOF object-oriented finite element software, developed at the National Institute of Standards and Technology, provides an interactive FEM tool which packages sophisticated mathematical capabilities with a user-interface that speaks the language of materials science...
Why You Should Care About Crystals
21 Aug 2022 | | Contributor(s):: Aerielle Rodriguez, Rice University
Why are Crystals important for material engineering? This project tested different crystals with varying band gaps in order to demonstrate the relationship between observable optical properties and physical properties of crystals.
Properties of Nanomaterials
30 Jul 2022 | | Contributor(s):: Mariel Kolker, Peter Kazarinoff, NACK Network
Ultrafast Spectroscopy of Nanomaterials
17 Jun 2022 | | Contributor(s):: Aziz Boulesbaa
Colorful and Smart Nanoscale Materials
17 Jun 2022 | | Contributor(s):: Yadong Yin
Integrating Microelectronics Contexts into Engineering Classrooms: Thermo-Calc Online Tool for the Design of Solder Materials
20 May 2022 | | Contributor(s):: Congying Wang
This presentation will first present how industrial soldering practices can be contextualized into current engineering classrooms, especially in materials science, to provide students with situated learning experiences. Then we will demonstrate how Thermo-Calc can be utilized as an effective...
May 11 2022
Integrating microelectronics contexts into engineering classrooms: Thermo-Calc online tool for the design of solder materials
Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop
06 May 2022 | | Contributor(s):: Kevin Greenman
Chemprop is an open-source implementation of a directed message passing neural network (D-MPNN) that has been demonstrated to be successful in predicting a variety of molecular properties, including solvation properties, optical properties, infrared spectra, and toxicity....
URE Experience - DFT Thermoelectric Calculations
15 Apr 2022 | | Contributor(s):: Gustavo Javier
Gustavo discusses his experience in the 2015 NCN URE program and his work to develop a thermoelectric simulation for the nanoHBU tool DFT Material Properties Simulator . Gustavo Javier now teaches high school physics in the Los Angeles area.The DFT Material Properties Simulator can compute...
LAMMPS Data File Generator Tool Demo
15 Apr 2022 | | Contributor(s):: Carlos Miguel PatiÃ±o
A quick demonstration of the nanoHUB tool LAMMPS Data-File Generator. This was developed as part of the 2017 NCN URE program.
Learning and Teaching Data Science using nanoHUB’s Cloud Resources
18 Mar 2022 | | Contributor(s):: Alejandro Strachan
This talk will discuss how data science is accelerating innovation in STEM fields. These tools enable the efficient handling of valuable data, the identification of patterns in large data collections, the development of predictive models, and the optimal design of experiments.
Quinn Michael Grudzinski
out of 5 stars
10 Feb 2022 | | Contributor(s):: Kei Yamamoto
A tutorial for the tool OOF2 - Finite Element Analysis of MicrostructuresOOF2 is designed to help materials scientists calculate macroscopic properties from images of real or simulated microstructures. It reads an image, assigns material properties to features in the image, and conducts virtual...
Lessons from Nanoscience: A Lecture Notes Series
31 Jan 2022 | | Contributor(s):: Mark Lundstrom (editor)
The focus of the series is on electronics, but volumes in areas of nanoscience and technology broadly related to electronics will be also be considered, as long as they are driven by a quest for unifying principles that embed a diversity of phenomena or techniques.
Characterization - Atomic Force Microscopy
12 Jan 2022 | | Contributor(s):: Wesley C. Sanders, NACK Network