This set of simulation tools has been developed for use with a course originally developed at UC Berkeley, taught by Jeffrey Grossman, which provides students with the fundamentals of computational problem-solving techniques that are used to understand and predict properties of nanoscale systems. Emphasis is 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.

These tools have been updated throughout spring term of 2011. The following simulations are run by the tool:

• Averages and Error Bars
• Molecular Dynamics (Lennard-Jones)
• Molecular Dynamics (Carbon Nanostructures)
• Monte Carlo (Hard Sphere)
• Monte Carlo (Ising Model)
• Quantum Chemistry (GAMESS)
• Quantum Chemistry (Quantum Espresso)
• Density Functional Theory (Siesta)
• Quantum Monte Carlo (QWalk)

Any questions, comments, difficulties should be directed to Jeff.

Development Team: David Strubbe, Daniel Richards, Elif Ertekin, Jeff Grossman, Justin Riley.

Software Tools for Academics and Researchers (http://web.mit.edu/star)

Office of Educational Innovation and Technology (http://oeit.mit.edu)

Massachusetts Institute of Technology (http://web.mit.edu)

Researchers should cite this work as follows:

• daniel richards; Elif Ertekin; Jeffrey C Grossman; David Strubbe; Justin Riley (2013), "MIT Atomic Scale Modeling Toolkit," https://nanohub.org/resources/ucb_compnano. (DOI: 10.4231/D3833MZ2R).

  BibTex | EndNote

1. ab initio
2. band structure
3. computational chemistry
4. computational materials
5. computational science/engineering
6. density functional theory
7. molecular simulations
8. Monte Carlo
9. nanoelectronics
10. nanomaterials
11. NCN@Berkeley Supported
12. NCN Supported
13. quantum Monte Carlo