Graduate Courses

Summer 2020

MSE1065H – Application of Artificial Intelligence in Materials Design

Instructor: Chandra Veer Singh
Start date: June 22, 2020
End date: July 10, 2020
Days of the week: All weekdays (2 weeks of lectures and tutorials)
Time: 10 am-12 pm Lectures; 2 pm – 4 pm Labs (Computational)
Primary teaching method: LEC + PRA (Online, via Quercus)


In this course students will be exposed to the applications of machine learning for materials design, including physical metallurgy, catalysis and mechanics of materials. We will begin by conducting a review of statistical and numerical methods, and programming in R and Python. Then, the most important machine learning techniques of relevance to materials science will be described. This will include linear, nonlinear and logistic regression, decision trees, artificial neural networks, deep learning, supervised and unsupervised learning. Thereafter, the students will be provided hands-on experience on analyzing data and apply ML approaches through a set of case studies, pertaining to alloy design, additive manufacturing, and catalyst design. Finally, students will apply these skills through a term project on materials science problem of their interest.

This course has been selected for Data Analytics emphasis in FASE at the graduate level. Due to the broad nature of course topics, we encourage students from Chem Eng, MIE, Chemistry, and other departments.

Final date to drop course without academic penalty: July 1, 2020

Minimum Enrollment: 5
Maximum Enrollment: 30
Course Text: No course text
No final exam. There will be weekly quizzes, during the PRA times.

MSE1022H – Application of Category Theory in Materials Science

Instructor(s): Glenn Hibbard, Charlie Katrycz and Fabian Parsch
Start date: May 27, 2020
End date: August 5, 2020
Days of the week: Wednesdays (8 weeks of lectures and tutorials)
The first four weeks (May 27, June 3, June 10, and June 17) will be conducted before the MSE 1065H course, while the second four weeks (July 15, July 22, July 29, and August 5) will be conducted afterwards.
Time: Lectures 3 pm-4:30 pm and Tutorials 4:30 pm-6 pm
Primary teaching method: LEC + TUT (Online, via Quercus)

Course Deliverables
There are no exams in this course. Instead, each student will develop a major project based on a case study material system of their own choosing. Broad flexibility will be given each student in terms of how they will categorically deconstruct their own system against one of several analytical themes.

Course Description
One of the most difficult steps for a developing materials specialist is to be able to think and model in the language of multi-scaled dynamical state spaces. In this framework, features of energy are categorized on the basis of their topological and geometric character with their interactions deconstructed in order to define the boundary between known and unknown for a given system. This can be a difficult learning step because every material system requires its own unique deconstruction, meaning that there is no single axiomatic template to follow, and fundamentally different thinking approaches are to be taken depending on whether the engineering or science side of the materials question is to be emphasized. The goal of the course is to accelerate the development of the multi-scaled thinking process, by having students deconstruct a materials system of their own choosing according to the conceptual frameworks discussed in class: Category Theory, Information Theory, Assemblage Theory, and Perturbation Theory.

Due to the broad nature of course topics and the individualized nature of each student project, students from outside of MSE (e.g. Chem Eng, MIE, Chemistry, and other departments) are encouraged to participate.

Minimum Enrollment: 5
Maximum Enrollment: 30
Course Text: No course text