- Computational Nanoscience, Lecture 1: Introduction to Computational Nanoscience
- Computational Nanoscience, Lecture 2: Introduction to Molecular Dynamics
- Computational Nanoscience, Lecture 13: Introduction to Computational Quantum Mechanics
- Computational Nanoscience, Lecture 18: Density Functional Theory and some Solid Modeling
- Computational Nanoscience, Homework Assignment 3: Molecular Dynamics Simulation of Carbon Nanotubes
- Computational Nanoscience, Lecture 3: Computing Physical Properties
- Computational Nanoscience, Lecture 7: Monte Carlo Simulation Part I
- Nano*High: Nanoscience for High School Students
- Computational Nanoscience, Lecture 8: Monte Carlo Simulation Part II
- Electron and Ion Microscopies as Characterization Tools for Nanoscience and Nanotechnology
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 course will provide students with the fundamentals of computational problem-solving techniques that are used to elucidate the atomic-scale behavior of energy conversion and storage nano-materials.
Professor of Computational Materials Science
Department of Materials Science and Engineering
Department of Mechanical Engineering
My overall strategy and approach to scientific problems is to use theory and simulation to gain fundamental understanding, develop new insights based on this understanding, and use these insights to develop new materials with improved properties working closely with experimental groups at each step.
In general, I strive to apply cutting-edge algorithm development to outstanding science and technology challenges, with emphasis on understanding and predicting new materials for advances in solar energy, thermoelectrics, energy storage, sensing, and synthesis. My approach does not necessarily rely on a single method to solve a given problem nor do I favor one computational approach over another; rather, I believe in using the best method(s) available and those most suited to tackle the challenge at hand. The vision I set for my research program includes a strong emphasis on a multidisciplinary approach in order to expand the scientific possibilities beyond any one discipline or field, including (1) structuring regular interactions between my group and other theoretical and experimental research groups (faculty and students) from chemistry, physics, materials science, computer science, chemical engineering, mechanical engineering, and electrical engineering departments, (2) encouraging and facilitating my group members to promote collaborations with experimental colleagues with whom they share science interests, and (3) creating diversity in my group by taking students and postdocs in overlapping but diverse backgrounds in order to maximize our working phase space. I believe that this approach also allows students in my group to gain exposure to a broad intersection of computational materials science.
Whenever possible I strive to work on problems related to materials that are directly relevant to global challenges; therefore in addition to cross-disciplinary collaborations related to my research goals, I actively pursue key partnerships with colleagues from both academia and industry, that help me understand whether we are working on materials breakthroughs that could one day be scalable, robust and compatible with existing infrastructure and manufacturing to the extent possible. Unsolved fundamental scientific problems will have a tremendous impact on many of the global challenges we face today, such as those related to energy and the environment. For example, the limits of quantum efficiency in abundant materials, the limits of energy density for storage materials, and the limits of thermal transport, are all largely unknown in many materials and yet critically important to their behavior. These are the kinds of fundamental problems I am interested in.
Researchers should cite this work as follows:
Jeffrey C Grossman; Alexander S McLeod (2009), "Computational Nanoscience for Energy," https://nanohub.org/resources/7350.