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Teaching Materials (1-20 of 32)

1. Ionization Potential of Small Molecules Using DFT

   Teaching Materials | 27 Aug 2018 | Contributor(s):: Alejandro Strachan

   Use DFT simulations to explore the ionization potential (energy required to remove an electron) in atoms and small molecules. Disclaimer: While very powerful, DFT makes well known approximations and the results obtained in this module are approximate. 

2. Scaffolding Simulations in a Rate Processes of Materials Course

   Teaching Materials | 16 Aug 2018 | Contributor(s):: Susan P Gentry

   This learning resource describes a set of programming assignments that are used in a Rate Processes of Materials course. The assignments are designed around the pedagogical principle of scaffolding, in which students are given initial support structures that are gradually removed. The...

3. Using DFT to Predict the Equilibrium Lattice Parameter and Bulk Modulus of Crystalline Materials

   Teaching Materials | 23 Aug 2017 | Contributor(s):: André Schleife, Materials Science and Engineering at Illinois

   This activity guides users through the use of DFT calculations with Quantum ESPRESSO in nanoHUB to calculate the total energy of a crystal structure.  By varying the volume of the structure, and calculating the associated energies, the equilibirum structure can be found.  Users are...

4. Using DFT to Simulate the Band Structure and Density of States of Crystalline Materials

   Teaching Materials | 23 Aug 2017 | Contributor(s):: André Schleife, Materials Science and Engineering at Illinois

   In this activity, DFT is used to simulate the band structure and density of states of several crystalline semiconductors.  Users are instructed in how to use the Bilbao Crystallographic Server to select a path through the Brillouin zone for each structure. This activity is adapted from an...

5. Comparing the Operation of p-i-n vs. p-n Junction Diodes Using PN Junction Lab in ABACUS

   Teaching Materials | 23 Aug 2017 | Contributor(s):: André Schleife, Materials Science and Engineering at Illinois

   In this activity, students use the PN Junction Lab simulation tool in ABACUS on nanoHUB to simulate different p-i-n or p-n diode structures.  Plots of hole concentration and electric field as a function of position, along with the gand structure with and without applied bias, will be...

6. Computer Modeling Module: Chemical Reaction Simulation using SIESTA

   Teaching Materials | 23 Aug 2017 | Contributor(s):: Lan Li

   This activity guides students through a module using the SIESTA DFT tool that is housed within the MIT Atomic Scale Modeling Toolkit on nanoHUB. Instructional videos, background reading, reminders and the assignment are included. Learning outcomes: Get familiar with SIESTA tool and activation...

7. DFT

   Teaching Materials | 17 Jan 2017

8. Thermodynamics

   Teaching Materials | 17 Jan 2017

9. Mechanics

   Teaching Materials | 17 Jan 2017

10. Kinetics

    Teaching Materials | 17 Jan 2017

11. mage:ic:kinetics1 - Diffusion in 1D and 3D

    Teaching Materials | 10 Mar 2014 | Contributor(s):: Michael L. Falk

    This module guides students through two analyses of diffusion problems using the COMSOL finite element software. Students are then asked to use what they have learned to guide the design of a drug delivery device.Disciplinary Goals: Understand mass transport in 1D and 3D, effects of boundary...

12. Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I

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    Teaching Materials | 15 May 2008 | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    This lecture provides and introduction to Quantum Monte Carlo methods. We review the concept of electron correlation and introduce Variational Monte Carlo methods as an approach to going beyond the mean field approximation. We describe briefly the Slater-Jastrow expansion of the wavefunction, and...

13. Computational Nanoscience, Lecture 21: Quantum Monte Carlo, part II

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    Teaching Materials | 15 May 2008 | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    This is our second lecture in a series on Quantum Monte Carlo methods. We describe the Diffusion Monte Carlo approach here, in which the approximation to the solution is not restricted by choice of a functional form for the wavefunction. The DMC approach is explained, and the fixed node...

14. Computational Nanoscience, Pop-Quiz

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    Teaching Materials | 15 May 2008 | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    This quiz summarizes the most important concepts which have covered in class so far related to Molecular Dynamics, Classical Monte Carlo Methods, and Quantum Mechanical Methods.University of California, Berkeley

15. Computational Nanoscience, Pop-Quiz Solutions

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    Teaching Materials | 15 May 2008 | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    The solutions to the pop-quiz are given in this handout.University of California, Berkeley

16. Computational Nanoscience, Lecture 23: Modeling Morphological Evolution

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    Teaching Materials | 15 May 2008 | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    In this lecture, we present an introduction to modeling the morphological evolution of materials systems. We introduce concepts of coarsening, particle-size distributions, the Lifshitz-Slyozov-Wagner model, thin film growth modes (Layer-by-Layer, Island growth, and Stranski-Krastanov), and...

17. Computational Nanoscience, Lecture 26: Life Beyond DFT -- Computational Methods for Electron Correlations, Excitations, and Tunneling Transport

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    Teaching Materials | 16 May 2008 | Contributor(s):: Jeffrey B. Neaton

    In this lecture, we provide a brief introduction to "beyond DFT" methods for studying excited state properties, optical properties, and transport properties. We discuss how the GW approximation to the self-energy corrects the quasiparticle excitations energies predicted by Kohn-Sham DFT. For...

18. Computational Nanoscience, Lecture 27: Simulating Water and Examples in Computational Biology

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    Teaching Materials | 16 May 2008 | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    In this lecture, we describe the challenges in simulating water and introduce both explicit and implicit approaches. We also briefly describe protein structure, the Levinthal paradox, and simulations of proteins and protein structure using First Principles approaches and Monte Carlo...

19. Computational Nanoscience, Lecture 28: Wish-List, Reactions, and X-Rays.

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    Teaching Materials | 16 May 2008 | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    After a brief interlude for class feedback on the course content and suggestions for next semester, we turn to modeling chemical reactions. We describe chain-of-state methods such as the Nudged Elastic Band for determining energy barriers. The use of empirical, QM/MM methods are described. We...

20. Computational Nanoscience, Lecture 29: Verification, Validation, and Some Examples

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    Teaching Materials | 16 May 2008 | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    We conclude our course with a lecture of verification, and validation. We describe what each of these terms means, and provide a few recent examples of nanoscale simulation in terms of these concepts.University of California, Berkeley