Teaching

NANO 106 – Crystallography of Materials

This undergraduate class provides students with a solid foundation in the fundamentals of crystallography and symmetry, and their implications on properties of materials. The syllabus includes: crystal lattice systems and planes; symmetry and group theory; derivation of point, plane and space groups; interpreting the International Tables for Crystallography; tensor properties of materials; and diffraction techniques (X-ray, neutron and electron) for structure and spacegroup determination.

More information and materials is at the NANO106 wiki and the course Github repo.

The next session for this class is in Spring 2017.

CENG/NANO 114 – Probability and Statistical Methods for Engineers

This undergraduate class provides a pragmatic overview of probability and statistical methods for engineers. Less attention is paid to mathematical theory, and more emphasis is placed on the actual application of probabilistic reasoning and statistical analysis to engineering design and interpretation of scientific results. Topics covered include: sets, sample spaces and the axioms of probability; conditional probability and Bayes’ Rule; discrete and continuous random variables; expectation, moments and variance; the Normal distribution and the Central Limit Theorem; correlation and linear regression; hypothesis testing.

More information and materials is at the CENG114 wiki and the course Github repo.

The next session for this class is in Winter 2017.

NANO 266 – Quantum Mechanical Modelling of Materials and Nanostructures

This graduate class provides students with a highly practical introduction to the application of first principles quantum mechanical simulations to model, understand and predict the properties of materials and nano-structures. The syllabus includes: a brief introduction to quantum mechanics and the Hartree-Fock and density functional theory (DFT) formulations; practical simulation considerations such as convergence, selection of the appropriate functional and parameters; interpretation of the results from simulations, including the limits of accuracy of each method. Several lab sessions provide students with hands-on experience in the conduct of simulations. A key aspect of the course is in the use of programming to facilitate calculations and analysis.

The next session for this class is in Spring 2017 and slides are available on slideshare.