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Materials Courses and Tutorials
Undergraduate Courses
MSEN 201 Introduction to Materials Science & Engineering
Introduction to Materials Science and Engineering at Texas A&M (2016), Taught by Patrick J Shamberger
The 39 lectures in this award-winning course introduce all of the topics typically covered in an introductory level materials science course: Atomic Structures, Bonding, Crystalline Solids, Crystal Structures of Metals, Crystal Structures of Ceramics, Structure of Polymers, Phase Diagrams, Defects, Diffusion, Mechanical Properties of Metals, Deformation and Strengthening, Electrical Properties, Thermal Properties, Magnetic Properties, and Optical Properties. The course content is aligned with Callister's textbook, which is fairly similar to that of Shackelford.
This introductory MSE course is also available in the nanoHUB-U style format.
Illinois MATSE 280: Introduction to Engineering Materials
MATSE 280 at University of Illinois, Urbana- Champaign (2008)
Taught by Duane Douglas Johnson
This course introduces you to the materials science and engineering of metals, ceramics, polymers, and electronic materials. Topics include: bonding, crystallography, imperfections, phase diagrams, properties and processing of materials. Case studies are used when appropriate to exemplify the lecture topics. Related courses are mostly focused on Mechanical Behavior.
Tutorials
Use this link to find content related to Data Science.
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Machine Learning for Materials Science, Part 1
Data science and machine learning are playing increasingly important role in materials science and engineering. This online tool provides examples of the use of these tools in the field of materials science, looking at . These Jupyter notebook tutorials contain step-by-step explanations of each activity using live code that can be easily modified and tested to aid in learning.
The initial set of tutorials focus on:
i) Data query, organization and visualization,
ii) Developing a simple model using linear regression to explore correlations between materials properties, and
iii) Neural network (NN) models trained to predict materials properties, such as the modulus of elasticity, from basic element properties.
iv) NN models to classify materials; predicting their crystal structure based on other elemental properties.
Hands on Learning Modules for Data Science and Machine Learning for Engineering
This series of modules introduces key concepts in data science in the context of application in materials science and engineering. The end to end modules include a recorded lecture, hands-on tutorial, and exercises to try.
Tensor Flow Tutorial
This set of tutorials using Jupyter notebooks in nanoHUB that will get you started with machine learning using TensorFlow and Keras. The set of tutorials was taken from TensorFlow (https://www.tensorflow.org/tutorials/) with copyright by François Chollet and is deployed with minimal modifications. Using nanoHUB resources users can run the tutorials, modify them and explore machine learning from any laptop or tablet, without downloading or installing any software.
nanoHUB-U Courses
nanoHUB-U courses are designed for the practicing engineer, upper division undergraduate or new graduate student who wants to learn cutting-edge content in a self-paced, short course format.
An archived version of this course can be viewed on edX. The original nanoHUB-U version is also available:
From Atoms to Materials— Predictive Theory and Simulations
Taught by Alejandro Strachan
This five-week short course covers the basic physics that govern materials at atomic scales.
Course Description: From Atoms to Materials: Predictive Theory and Simulations is a five-unit online course that develops a unified framework for understanding essential physics that govern materials at atomic scales and relate these processes to the macroscopic world.
Topics covered include: Basic quantum mechanics, quantum well, hydrogen atom, multielectron atoms, the nature of the chemical bond, LCAO, electronic structure of crystals, electronic band structures, Molecular Dynamics (MD), Interatomic potentials for MD, statistical mechanics, Ab Initio electronic structure calculations, Hartree-Fock and Exchange Interaction, Density Functional Theory (DFT).
Fundamentals of Atomic Force Microscopy, Part 1— Fundamental Aspects of AFM
Taught by Ron Reifenberger
Selected Topics: non-contact tip-surface interactions, intra/inter molecular interactions, contact tip-surface interactions, AFM components/calibrations, force spectroscopy, contact mode Imaging, VEDA
Fundamentals of Atomic Force Microscopy, Part 2— Dynamic AFM Methods
Taught by Arvind Raman
Selected Topics: dynamic AFM, using VEDA, reconstructing surface forces, dynamic AFM for electrostatics, magnetics and biology.
Introduction to the Materials Science of Rechargeable Batteries
Taught by R. Edwin Garcia
Selected Topics: The battery potential, energy and power in a battery, battery figures of merit, electrochemical potential and equilibrium, thermal effects, tortuosity in porous media, reversible and irreversible interfacial reactions, battery architecture and design guidelines, advanced battery architectures.
Thermal Energy at the Nanoscale
Taught by Timothy S Fisher
Selected Topics: lattice structure, phonons, electron, carrier statistics, thermal properties, Landauer transport formalism, carrier scattering, transmission.
Thermoelectricity- From Atoms to Systems
Taught by Ali Shakouri, Supriyo Datta, and Mark Lundstrom
Selected Topics: fundamental concepts, Seebeck, Peltier, Thomson Effects, Termoelectric Transport Parameters, Nanoscale and Macroscale Characterization, Thermoelectronic Systems, Thermionics, Semiconductors with Embedded Nanoparticles, State of the Art Thermoelectric Materials.
Computational Materials Science and Engineering
MSE 498 at the University of Illinois at Urbana-Champaign (2015). 19 Lectures.
Taught by Andrew Ferguson
This new course will give students hands-on experience with popular computational materials science and engineering software through a series of projects in: electronic structure calculation (e.g., VASP), molecular simulation (e.g., GROMACS), phase diagram modeling (e.g., Thermo-Calc), finite element modeling (e.g., OOF2), and materials selection. The course will familiarize students with a broad survey of software tools in computational materials science, scientific computing, and prioritize the physical principles underlying the software to confer an understanding of their applicability and limitations.
Short Course on Molecular Dynamics Simulation
Taught by Ashlie Martini
This set of ten presentations accompanied a graduate level course on Molecular Dynamics simulation. The specific objective of the course (and the presentations) is to provide:
1. Awareness of the opportunities and limitations of Molecular Dynamics as a tool for scientific and engineering research
2. Understanding of the compromise between model complexity/realism and computational expense
3. Background that enables interpretation of Molecular Dynamics-based studies reported in the literature
Nanomaterials
MSE 376 at Northwestern University (2005). 19 Lectures.
Taught by Mark Hersam
Selected Topics: film deposition, lithography, chemical synthesis, carbon nanomaterials, SPM lithography, nanoscale CMOS, nanomagnetism, nanoscale thermal properties, nanoelectromechanical systems
Physics of Solids
MSE 405 at Northwestern University (2006). 35 Lectures.
Taught by Mark Hersam
Introduction to quantum mechanics and solid state physics. Specific topics include free electron behavior, potential energy wells and barriers, energy band theory, phonons, and electrical properties of metals and semiconductors. This course develops many concepts of fundamental interest to nanoscale science and engineering such as quantum confinement and reduced dimensionality effects in nanomaterials.
Overview of Computational Nanoscience
Physics C203 and NSE C242 at UC Berkeley (2008). 29 Lectures.
Taught by Jeffrey C. Grossman and Elif Ertekin
This course will provide students with the fundamentals of computational problem-solving techniques that are used to understand and predict properties of nanoscale systems. Emphasis will be placed on how to use simulations effectively, intelligently, and cohesively to predict properties that occur at the nanoscale for real systems. The course is designed to present a broad overview of computational nanoscience and is therefore suitable for both experimental and theoretical researchers.
Atomic-Scale Simulation
MSE 376 at Northwestern University (2005). 19 Lectures.
Taught by David M. Ceperley
THE OBJECTIVE is to learn and apply fundamental techniques used in (primarily classical) simulations in order to help understand and predict properties of microscopic systems in materials science, physics, chemistry, and biology.
An Introduction to Molecular Dynamics
MSE 597G at Purdue University (2008). 10 Lectures
Taught by Alejandro Strachan
Selected Topics: classical mechanics, statistical mechanics, nano-materials simulation toolkit, interatomic potentials, molecular dynamics simulations, reaction zone model, VKML
Introduction to Uncertainty Quantification
The objective of this summer school on Uncertainty Quantification and its Applications is to present an accessible introduction to the basic tools of uncertainty quantification, with the goal of orienting attendees to the field and helping them address UQ questions in their application areas of interest. Lectures will provide basic introductions to probability and stochastic processes, data analysis, estimation and inference, sensitivity analysis, uncertainty propagation, sampling methods, Bayesian computation, experimental design, and model validation. In addition, several guest lecturers will present a diverse set of applications and snapshots of current research, in which uncertainty quantification plays an important role.
Uncertainty Quantification in Materials Modeling
This is the seminar portion of the Network for Computational Nanotechnology (NCN) and NEEDS (Nano-Engineered Electronic Devices Simulation) 2015 Summer School consisting of presentations related to uncertainty quantification.
Resources in other nanoHUB Topical Groups
Characterization and Metrology Group
Courses on AFM, TEM, Optical Microscopy, etc.
Nanoelectronics Group
Courses on semiconductor device physics and nanoelectronics
Workshops and summer schools on Computational Materials Science
at the University of Illinois at Urbana-Champaign.
Integrated Computational Materials Science Education Summer School (ICMEd)
University of Michigan's Computational MSE training resources. More to come!
Machine Learning
Hands-on tutorial series on advanced data science and machine learning techniques for research.
Introductory modules on data science and machine learning.
Magnetics Simulations with OOMMF
Hands-on tutorial with videos and active discussion board that supports researchers getting started with micromagnetic simulations.