4131 - AI and Materials Databases for Self-Driving Labs
Course Description
Unleash the power of data science in the realm of self-driving laboratories. This remote, asynchronous course empowers you to apply data science concepts to materials discovery tasks. You’ll create Bayesian optimization scripts, explore advanced optimization topics, and adapt templates to create an advanced optimization setup for a materials discovery task. Topics will include multi-objective, constrained, high-dimensional, multi-fidelity, batch, asynchronous, and domain-aware Bayesian optimization. Additionally, you’ll learn to share your findings by uploading datasets to a data repository, creating benchmark models, and hosting models on data science platforms.
This course is presented in partnership with the Acceleration Consortium at the University of Toronto.
This is an online, self-directed course, and you can work through the modules at your own pace. You can expect to complete the course in a month but have up to 1 year to complete it.
Within 4-6 weeks of successfully completing this course, you will receive your micro-credential indicating achievement of the outlined learning outcomes and competencies/skills. Micro-credentials are tamper proof, verifiable, blockchain-based and 100% digital. They can be shared on social media, including LinkedIn and Facebook, embedded in websites or downloaded as PDFs.
Learning Outcomes
By the end of this micro course, you'll be able to:
- Describe and categorize a materials discovery task using data science language and concepts
- Customize a Bayesian optimization script to systematically identify the optimal chocolate chip cookie recipe, demonstrating practical application of optimization techniques
- Evaluate and select an advanced optimization setup that is best suited for a specific materials discovery task, showcasing critical analysis and decision-making skills
- Develop and execute a program to upload a dataset to a public database, construct a benchmark model, and deploy it online, illustrating proficiency in data sharing and model hosting
Competencies/skills developed in this micro course include:
- Data science literacy
- Bayesian optimization
- Advanced Bayesian optimization
- Workflow orchestration
- Benchmarking
Notes
No withdrawals are permitted after enrolment.
Eligible learners may apply to the Ontario Student Assistance Program (OSAP) for this micro-credential. You can find more information on our Financial Aid page.
Prerequisites
The recommended prerequisite for this course is 4010 Introduction to AI for Discovery using Self-driving Labs.
For participants to complete this course within the expected timeframe (approx. 27 hours), at least beginner proficiency in Python programming is recommended. Those with advanced programming expertise will likely require a significantly shorter amount of time, whereas those with no prior programming experience may require 50 hours or more.
This course may be applied towards the SCS Certificate(s) in
- Autonomous Systems for Discovery : Required courses