Stretching simulation of an alpha-helical protein domain
Tools | 10 Jan 2011 | Contributor(s): Markus Buehler, Justin Riley, Joo-Hyoung Lee, Jeffrey C Grossman
Uses steered molecular dynamics (SMD) to apply a tensile load to the ends of a molecule (such as an alpha-helical protein domain)
Crack Propagation Lab
Tools | 06 Dec 2010 | Contributor(s): Markus Buehler, Justin Riley, Joo-Hyoung Lee, Jeffrey C Grossman
Models supersonic crack propagation in a 2D triangular lattice
Stretching Simulation of FCC Crystal
Tools | 18 Nov 2010 | Contributor(s): Markus Buehler, Justin Riley, Joo-Hyoung Lee, Jeffrey C Grossman
This tool simulates a continuous expansion of an FCC crystal while measuring the energy, stresses, etc
Amorphous Silicon Generator
Tools | 21 Oct 2010 | Contributor(s): Eric Carl Johlin, Lucas Wagner, Jeffrey C Grossman, Justin Riley, David Strubbe, Vardhan Solanki
a-Si:H Generator
Nanowire Tensile Deformation Lab
Tools | 17 Aug 2010 | Contributor(s): Markus Buehler, Justin Riley, Joo-Hyoung Lee, Jeffrey C Grossman
Simulates tensile deformation of a copper nanowire
nanoHUB PhotoVoltaics Reference Zone
Teaching Materials | 19 Jan 2010 | Contributor(s): Alexander S McLeod, Jeffrey B. Neaton, Jeffrey C Grossman
Need information on the science of photovoltaics and solar cell technology? Find it here!The nanoHUB PhotoVoltaics Reference Zone is the right destination for finding general information about photovoltaic solar cell science and technology, as well as for viewing news articles and getting...
Nanostructured Optoelectronics Toolbox
Tools | 19 Oct 2009 | Contributor(s): Ian Michael Rousseau, Jeffrey C Grossman, Vladimir Bulovic, Polina Anikeeva
Examine charge and exciton transport in nanostructured optoelectonic devices
MIT Tools for Energy Conversion and Storage
Tools | 13 Sep 2009 | Contributor(s): Jeffrey C Grossman, Joo-Hyoung Lee, Varadharajan Srinivasan, Alexander S McLeod, Lucas Wagner
Atomic-Scale Simulation Tools to Explore Energy Conversion and Storage Materials
Computational Nanoscience for Energy
Teaching Materials | 10 Sep 2009 | Contributor(s): Jeffrey C Grossman, Alexander S McLeod
Materials for energy conversion and storage can be greatly improved by taking advantage of unique effects that occur at the nanoscale. In many cases, these improvements are due to fundamental microscopic mechanisms that can be understood and predicted by cutting-edge simulation methods. This...
Nano*High: Nanoscience for High School Students
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Series | 02 Feb 2010 | Contributor(s): Alexander S McLeod, Jeffrey B. Neaton, Jeffrey C Grossman
The Materials Sciences Division at the University of California's Lawrence Berkeley National Laboratory invites you and your students to Nano*High, a series of free Saturday morning lectures by UC Berkeley professors and LBNL senior scientists conducting research from nanoscience to molecular...
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
Computational Nanoscience, Lecture 28: Wish-List, Reactions, and X-Rays.
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...
Computational Nanoscience, Lecture 27: Simulating Water and Examples in Computational Biology
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...
Computational Nanoscience, Lecture 23: Modeling Morphological Evolution
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...
Computational Nanoscience, Pop-Quiz Solutions
The solutions to the pop-quiz are given in this handout.University of California, Berkeley
Computational Nanoscience, Pop-Quiz
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
Computational Nanoscience, Lecture 21: Quantum Monte Carlo, part II
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...
Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I
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...
Computational Nanoscience, Lecture 19: Band Structure and Some In-Class Simulation: DFT for Solids
Teaching Materials | 30 Apr 2008 | Contributor(s): Jeffrey C Grossman, Elif Ertekin
In this class we briefly review band structures and then spend most of our class on in-class simulations. Here we use the DFT for molecules and solids (Siesta) course toolkit. We cover a variety of solids, optimizing structures, testing k-point convergence, computing cohesive energies, and...
Computational Nanoscience, Lecture 18.5: A Little More, and Lots of Repetition, on Solids
Here we go over again some of the basics that one needs to know and understand in order to carry out electronic structure, atomic-scale calculations of solids.
Computational Nanoscience, Lecture 16: More and Less than Hartree-Fock
In the lecture we discuss both techniques for going "beyond" Hartree-Fock in order to include correlation energy as well as techniques for capturing electronic structure effects while not having to solve the full Hartree-Fock equations (ie, semi-empirical methods). We also very briefly touch upon...
Computational Nanoscience, Lecture 15: In-Class Simulations: Hartree-Fock
Using a range of examples, we study the effect of basis set on convergence, the Hartree-Fock accuracy compared to experiment, and explore a little bit of molecular chemistry.
Computational Nanoscience, Lecture 14: Hartree-Fock Calculations
A description of the Hartree-Fock method and practical overview of its application. This lecture is to be used in conjunction with the course toolkit, with the Hartree-Fock simulation module.
Computational Nanoscience, Lecture 13: Introduction to Computational Quantum Mechanics
In this lecture we introduce the basic concepts that will be needed as we explore simulation approaches that describe the electronic structure of a system.
Computational Nanoscience, Lecture 18: Density Functional Theory and some Solid Modeling
Teaching Materials | 21 Mar 2008 | Contributor(s): Elif Ertekin, Jeffrey C Grossman
We continue our discussion of Density Functional Theory, and describe the most-often used approaches to describing the exchange-correlation in the system (LDA, GGA, and hybrid functionals). We discuss as well the strengths and weaknesses of the LDA and present some examples of its use. Finally, a...