Simon Fraser University
Engaging the World

Course Outlines

Fall 2024 - SEE 895 G100

Special Topics III (3)

Accelerated Materials Discovery

Class Number: 6200

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 4 – Dec 3, 2024: Fri, 9:30 a.m.–12:20 p.m.
    Surrey

  • Corequisites:

    SEE 896 or SEE 897.

Description

CALENDAR DESCRIPTION:

Special Topics in Sustainable Energy Engineering.

COURSE DETAILS:

Accelerated Materials Discovery for Energy Conversion and Storage

 Course Overview: Innovations in energy technologies heavily rely on the development of advanced energy materials, spanning applications across diverse energy sectors such as fuel cells, hydrogen storage, photovoltaic systems, and batteries. The accelerated discovery of materials is a critical step for the advancements of these sustainable energy technologies. In particular, by expediting the process of material discovery, researchers and engineers can identify compounds with superior properties, enabling the development of these energy technologies that are both more efficient and economically viable. 

In the quest for accelerated materials discovery, both theory and experimentation play complementary roles. Computational approaches provide a valuable foundation for predicting material properties. Through simulations and machine learning, a vast space of potential materials can be explored. More importantly, these computational frameworks help narrow down the possibilities, guiding experimentalists toward the most promising candidates for validation, so the time and resources required for extensive trial and error will be significantly reduced.

This course serves as a crucial bridge, providing students with an opportunity to actively engage in this intersection of theory and experiment for sustainable energy technologies. By applying a diverse set of computational tools, students gain hands-on experience in evaluating the performance of materials vital to the functioning of fuel cells, hydrogen storage systems, photovoltaic systems, and batteries. Moreover, this course provides the students with the skills and insights needed to contribute meaningfully to fostering innovation and driving the design and development of cutting-edge energy technologies, preparing them for impactful roles in the advancement of sustainable energy solutions.

Course Description: This course focuses on advance energy materials design in sustainable engineering, encompassing a broad range of applications such as fuel cells, hydrogen storage, photovoltaic systems, and batteries. Students explore the challenges of material design for sustainability by applying modelling techniques and machine learning within the realm of sustainable engineering practices. They explore different methods for computer-aided materials design, including materials simulation and machine learning. Students gain hands-on experience and apply computational tools for virtual materials design. Moreover, the course includes valuable insights from industry expert guest lectures from world-known companies such as Toyota Reserach Institute and Schrödinger, where students learn how to apply their insights for designing high performance energy systems, preparing them for real-world applications.

COURSE-LEVEL EDUCATIONAL GOALS:

Learning Outcomes: By the end of this course, students are expected to:

  • Understand the importance of materials design in the context of sustainable engineering.
  • Identify and address challenges specific to materials design for energy storage/conversion applications.
  • Comprehend the synergy between Materials Simulation and Machine Learning in the process of materials design.
  • Incorporate materials information effectively into ML models and apply the models to enhance predictive capabilities.
  • Demonstrate proficiency in collecting and pre-processing data from various databases.
  • Be able to use feature engineering techniques tailored for materials data to evaluate ML models’ performance and analyze the results.
  • Benefit from guest lectures by industry experts, gaining insights into current design efforts. 

Grading

NOTES:

Assessment/Grading:

The grading and assessment for this course are based on assignments, paper presentation and final course project and presentation.

 

 

% of Final Grade

Description

Assignments
 
30%
This involves an in-class activity format where assignments will be distributed to students for completion during the class sessions.
Course Project
 
 
40%
Students will receive a project assignment, focusing on either materials simulation/modelling or the application of ML models for predicting materials properties.
 
Paper Presentation
 
 
 
30%
Students will select two or more papers pertinent to materials design employing simulation or ML methods, based on their interest and alignment with the course. They will present their chosen papers to the class, showcasing their relevance to the subject matter.

REQUIREMENTS:

Prerequisites and Co-requisites:

Throughout the course, the Python programming language will be used, so having preliminary programming skills will be beneficial or it is expected that the students familiarize themselves with Python. It is also recommended that students have laptops or PCs to gain access to relevant software and computing resources for the laboratory components of the course.

Materials

MATERIALS + SUPPLIES:

  • Software and Computing Resources: Atomic Simulation Environment (ASE) and Quantum Espresso (QE) – which are open source.
  • Access to Databases and Materials Data Repositories: We will be using open sources materials databases.
  • Access to Supercomputer Cluster will be provided for all students.

RECOMMENDED READING:

Recommended Textbook: Accelerated Materials Discovery: How to Use Artificial Intelligence to Speed Up Development. De Gruyter, 2022

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html

RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.