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Jeffrey Grossman
| Contributions | 34 |
|---|---|
| Affiliation | University of California, Berkeley |
| Web Site | http://nano.berkeley.edu/coins/ |
| Biography | Jeffrey C. Grossman leads the new NSF Nanoscience and Engineering Center, COINS, at the University of California at Berkeley. He is charged with driving forward several large-scale sensing applications through highly interdisciplinary research approaches involving 30 faculty members at Berkeley, Stanford, and CalTech. In addition, Dr. Grossman heads the Computational Nanoscience Group at UC Berkeley, which is actively engaged in a number of research areas relating to the simulation of nanoscale materials and interfaces. His research focuses on the application and development of cutting-edge classical and quantum simulation tools to understand, predict, and design novel nanoscale materials with applications to: developing new sensing approaches, predicting new materials for efficient photovoltaics, examining the microscopic properties of water, understanding the growth mechanisms of carbon nanotubes and silicon nanowires, and designing controllable self-assembly processes of inorganic nanoscale building blocks. Dr. Grossman received his Ph.D. in theoretical physics from the University of Illinois, performed postdoctoral work at U.C. Berkeley, and was a Lawrence Fellow at the Lawrence Livermore National Laboratory, where he helped to initiate a large-scale computational nanoscience program. He is a widely published scientist, and has been in the business of simulating nanoscale materials for over 15 years. |
Contributions
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Berkeley Computational Nanoscience Class Tools
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Usage Stats Last 12 Months: Updated 16 May, 2008 more › Users: 160 Jobs: 8876 Avg. exec. time: 18 secs Reviews & Citations Google/IEEE Avg. Review: Citations: 0
24 Jan. 2008 | Tools | Contributor(s): Joe Ringgenberg, daniel richards, Elif Ertekin, Jeffrey Grossman
Tools for UC Berkeley Computational Nanoscience course, Spring 2008
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Computational Nanoscience, Homework Assignment 1: Averages and Statistical Uncertainty
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 36 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
30 Jan. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
The purpose of this assignment is to explore statistical errors and data correlation. This assignment is to be completed following lectures 1 and 2 using the "Average" program in the Berkeley Computational Nanoscience Toolkit.University of California, Berkeley
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Computational Nanoscience, Homework Assignment 2: Molecular Dynamics Simulation of a Lennard-Jones Liquid
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 25 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
15 Feb. 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey Grossman
The purpose of this assignment is to perform a full molecular dynamics simulation based on the Verlet algorithm to calculate various properties of a simple liquid, modeled as an ensemble of identical classical particles interacting via the Lennard-Jones potential. This assignment is to be …
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Computational Nanoscience, Homework Assignment 3: Molecular Dynamics Simulation of Carbon Nanotubes
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 44 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
15 Feb. 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey Grossman
The purpose of this assignment is to perform molecular dynamics simulations to calculate various properties of carbon nanotubes using LAMMPS and Tersoff potentials. This assignment is to be completed following lectures 5 and 6 using the "LAMMPS" program in the Berkeley Computational Nanoscience …
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Computational Nanoscience, Homework Assignment 4: Hard-Sphere Monte Carlo and Ising Model
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 20 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
05 Mar. 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey Grossman
In this assignment, you will explore the use of Monte Carlo techniques to look at (1) hard-sphere systems and (2) Ising model of the ferromagnetic-paramagnetic phase transition in two-dimensions. This assignment is to be completed following lecture 12 and using the "Hard Sphere Monte Carlo" and …
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Computational Nanoscience, Lecture 10: Brief Review, Kinetic Monte Carlo, and Random Numbers
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 33 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
05 Mar. 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey Grossman
We conclude our discussion of Monte Carlo methods with a brief review of the concepts covered in the three previous lectures. Then, the Kinetic Monte Carlo method is introduced, including discussions of Transition State Theory and basic KMC algorithms. A simulation of vacancy-mediated diffusion …
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Computational Nanoscience, Lecture 11: Phase Transitions and the Ising Model
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05 Mar. 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey Grossman
In this lecture, we present an introduction to simulations of phase transitions in materials. The use of Monte Carlo methods to model phase transitions is described, and the Ising Model is given as an example for modeling the ferromagnetic-paramagnetic transition. Some of the subtleties of …
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Computational Nanoscience, Lecture 12: In-Class Simulation of Ising Model
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24 Mar. 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey Grossman
This is a two part lecture in which we discuss the spin-spin correlation function for the the Ising model, correlation lengths, and critical slowing down. An in-class simulation of the 2D Ising Model is performed using the tool "Berkeley Computational Nanoscience Class Tools". We look at domain …
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Computational Nanoscience, Lecture 13: Introduction to Computational Quantum Mechanics
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05 May. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
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.
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Computational Nanoscience, Lecture 14: Hartree-Fock Calculations
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05 May. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
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.
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Computational Nanoscience, Lecture 15: In-Class Simulations: Hartree-Fock
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05 May. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
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.
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Computational Nanoscience, Lecture 16: More and Less than Hartree-Fock
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05 May. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
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 …
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Computational Nanoscience, Lecture 17: Tight-Binding, and Moving Towards Density Functional Theory
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 33 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
24 Mar. 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey Grossman
The purpose of this lecture is to illustrate the application of the Tight-Binding method to a simple system and then to introduce the concept of Density Functional Theory. The motivation to mapping from a wavefunction to a density-based description of atomic systems is provided, and the necessary …
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Computational Nanoscience, Lecture 18.5: A Little More, and Lots of Repetition, on Solids
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05 May. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
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.
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Computational Nanoscience, Lecture 18: Density Functional Theory and some Solid Modeling
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 27 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
24 Mar. 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey 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 …
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Computational Nanoscience, Lecture 19: Band Structure and Some In-Class Simulation: DFT for Solids
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05 May. 2008 | Teaching Materials | Contributor(s): Jeffrey 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 …
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Computational Nanoscience, Lecture 1: Introduction to Computational Nanoscience
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 68 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
15 Feb. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
In this lecture, we present a historical overview of computational science. We describe modeling and simulation as forms of "theoretical experiments" and "experimental theory". We also discuss nanoscience: "what makes nano nano?", as well as public perceptions of nanoscience and the "grey goo" …
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Computational Nanoscience, Lecture 2: Introduction to Molecular Dynamics
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30 Jan. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
In this lecture, we present and introduction to classical molecular dynamics. Approaches to integrating the equations of motion (Verlet and other) are discussed, along with practical considerations such as choice of timestep. A brief discussion of interatomic potentials (the pair potential and …
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Computational Nanoscience, Lecture 3: Computing Physical Properties
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 37 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
12 Feb. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
In this lecture, we'll cover how to choose initial conditions, and how to compute a number of important physical observables from the MD simulation. For example, temperature, pressure, diffusion coefficient, and pair distribution function will be highlighted. We will also discuss briefly the use …
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Computational Nanoscience, Lecture 4: Geometry Optimization and Seeing What You're Doing
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 64 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
13 Feb. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
In this lecture, we discuss various methods for finding the ground state structure of a given system by minimizing its energy. Derivative and non-derivative methods are discussed, as well as the importance of the starting guess and how to find or generate good initial structures. We also briefly …
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Computational Nanoscience, Lecture 5: A Day of In-Class Simulation: MD of Carbon Nanostructures
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 45 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
15 Feb. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
In this lecture we carry out simulations in-class, with guidance from the instructors. We use the LAMMPS tool (within the nanoHUB simulation toolkit for this course). Examples include calculating the energy per atom of different fullerenes and nantubes, computing the Young's modulus of a nanotube …
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Computational Nanoscience, Lecture 6: Pair Distribution Function and More on Potentials
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15 Feb. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
In this lecture we remind ourselves what a pair distribution function is, how to compute it, and why it is so important in simulations. Then, we revisit potentials and go into more detail including examples of typical functional forms, relative energy scales, and what to keep in mind when …
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Computational Nanoscience, Lecture 7: Monte Carlo Simulation Part I
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Usage Stats Last 12 Months: updated 01 May, 2008 Users: 54 Reviews & Citations Google/IEEE Avg. Review: Citations: 0
15 Feb. 2008 | Teaching Materials | Contributor(s): Jeffrey Grossman, Elif Ertekin
The purpose of this lecture is to introduce Monte Carlo methods as a form of stochastic simulation. Some introductory examples of Monte Carlo methods are given, and a basic introduction to relevant concepts in statistical mechanics is presented. Students will be introduced to the Metropolis …
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Computational Nanoscience, Lecture 8: Monte Carlo Simulation Part II
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15 Feb. 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey Grossman
In this lecture, we continue our discussion of Monte Carlo simulation. Examples from Hard Sphere Monte Carlo simulations based on the Metropolis algorithm and from Grand Canonical Monte Carlo simulations of fullerene growth on spherical surfaces are presented. A discussion of meaningful …
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Computational Nanoscience, Lecture 9: Hard-Sphere Monte Carlo In-Class Simulation
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20 Feb. 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey Grossman
In this lecture we carry out simulations in-class, with guidance from the instructors. We use the HSMC tool (within the nanoHUB simulation toolkit for this course). The hard sphere system is one of the simplest systems which exhibits an order-disorder phase transition, which we will explore with …