Prerequisites: Basic knowledge of C/C++ is required. Matlab, Python or Mathematica will also be used during the course lectures as well as in class assignments. Background required in differential equations, statistical and classical mechanics, and linear algebra.

Motivation
The emerging ability to economically arrange atoms in most of the ways permitted by physical law will revolutionize life, as we know it. Controlling the expression of material properties in any of its phases (i.e. solid, liquid, gas or plasma) and at any scale depends on its atomic composition and arrangement in a 2D or 3D structure, both of which originate at the nano-meter scale.

In order to understand, characterize, manipulate or optimize such properties one must study and elucidate the interactions between the different material building blocks or constituents, fundamentally atoms, which live in the nanometer scale. By today’s standards, this requires the simultaneous contribution from theory, computation and experiments engaging a wide range of technical fields, from physics, chemistry and biology, to computer science and engineering. This course takes a computational approach, based on first principles quantum mechanics and classical approximations, to address these issues.

Computational nano-science now supplements and complements physical experiments, and in many cases constitutes an enabling tool to study problems that are otherwise extremely expensive, dangerous or even impossible to address through experiments alone. As our theoretical understanding of matter and material phenomena improves, materials and processes “by design” will become the norm, and this will entail solving large-scale numerical problems to predict atomic structure, composition, grain interfaces, etc., from engineering or at-scale property specifications and the use of fundamentally-derived models at the heart of computational optimization engines.

Nanotechnology is not only an exciting interdisciplinary research area, but also a technological and commercial reality that will continue to drive and shape the future and health of humanity, environment, transportation, energy, and space systems, education and almost everything else that we can think of.

Description

The 16 week course will introduce the basic issues involved in nanotechnology and its broader applications, lay out the scientific end engineering foundations required to understand how the nano-scale atomic structure and composition of matter drives behavior, present the computational models and methods derived from first-principles quantum mechanics (QM), statistical mechanics, and classical mechanics and dynamics theory to characterize, design, and optimize materials, devices, systems and processes (biological, physical, chemical, etc.), and demonstrate their application for studying and solving critical challenges from a wide set of fields.

Objectives

After successful course completion, the attendant will:

• Achieve a conceptual, theoretical and applied understanding of how atomic structure and composition define material properties at all scales, and in particular at the nano-scale
• Acquire the necessary computational proficiency and knowledge about different first-principles based tools to study, characterize and optimize static and dynamic material properties, including but not limited to ab initio methods, density functional theory (DFT), adiabatic, non-adiabatic and coarse-grain force fields, molecular mechanics and molecular dynamics (MM/MD), Monte Carlo (MC) methods.
• Learn about applications to materials design, energy storage and retrieval, tissue engineering, drug design, space exploration, and others addressed using the first-principles computational approach presented in the course.
• Understand the broader impact of nano-scale science, engineering, and nanotechnology.

Contents

1. Introduction to nanoscale science and engineering (2 sessions)
   • Intentional manipulation of matter at the atomic scale
   • Nature as a source of inspiration to nanotechnology (and vice versa)
   • Current challenges in nanoscale science and engineering
   • Theory and computation as enabling tools in advancing nanotechnology
   • Time and length scales in atomic and sub-atomic motion
   • Implications of a nano-scale world
2. Theory and methods for first-principles based modeling and simulation (5 sessions)
   • Foundations of Quantum Mechanics – QM (ab initio methods)
   • Adiabatic approximations to QM (e.g. Hartree Fock Theory, Density Functional Theory)
   • Non-adiabatic approximations to QM (e.g. wave-packet methods)
   • Approximations from statistical and classical mechanics, force fields, molecular mechanics and dynamics
   • Rigid body mechanics and coarse-grain molecular mechanics/dynamics methods
   • Connecting to continuum methods (general observations)
3. Characterization of material properties using 1 st principles computational methods (5 sessions)
   • Structure prediction
   • Thermodynamics properties
   • Mechanical properties
   • Transport properties (includes electrical, optical, thermal)
   • Others (magnetic, rheological, tribological)
4. Class research project (1 session)
   • Specification, requirements and deliverables
5. Applications (3 sessions)
   • Health: Characterization and design of DNA nano-sequencers
   • Tissue engineering: Molecular scaffolds for cartilage tissue
   • Medicine: Nano drug deliver systems (DDS)
   • Proteomics: G-protein coupled receptor structure prediction, comformational analysis and ligand interactions
   • Materials characterization: Reaction kinetics during Portland cement hydration
   • Space exploration: Hypervelocity impact ionization fragmentation in space missions (Cassini-Hyugens)
   • Material synthesis in defense applications: Low-temperature atomic layer thin film deposition
   • Energy storage and retrieval: Cementituous materials for CO2 sequestration

NOTE: We will be using different modeling and simulation tools, among these:

• SeQuest (Sandia National Laboratory, SNL, http://dft.sandia.gov/Quest/)
• Quantum Espresso (http://www.quantum-espresso.org)
• LAMMPS (SNL, http://lammps.sandia.gov), incluyendo potenciales:
  • Non-adiabatic reactive force fields (http://lammps.sandia.gov/doc/pair_eff.html)
  • Adiabatic reactive force fields (ReaxFF, http://lammps.sandia.gov/doc/pair_reax_c.html)

How to study for this course

Attend lectures, read the scientific literature (list will be provided in class), and other relevant literature, practice simulation runs on local cluster, and solve all the assignments, including the class project individually (unless explicitly stated otherwise).

Evaluation