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Machine learning for (meta-)materials design

This is a short introductory course at the undergraduate/graduate level for introduction to machine learning (ML) for (meta-)materials design.

Is this course for you?

Have you heard about ML? Want to apply ML to your education/research in mechanics of materials? Don't want to invest a lot of time/effort in general ML courses?

Then this course may be appropriate for you as it provides just the sufficient detail to get you started with basic problems in ML for mechanics of materials.

At the end of the course, you should be able to:

• explain the fundamentals of regression,
• use PyTorch software for ML tasks,
• create and train deep neural networks,
• contextualize and deploy ML models to materials design applications.

How to get started?

The course contains 4 instructional sessions. The first three sessions focus on basics of ML, while the last session introduces a case study on design of metamaterials. If you are following the course via GitHub, it is recommended to read in detail the associated paper (along with the provided slides) for the case study in the fourth session.

For each session, workbooks and exercises (including solutions) are provided. In addition to the exercises, four assignments (each pertaining to one of the sessions) and a project (pertaining to the case study) are also provided.

Jupyter notebooks are primarily used for running the Python codes. Software installation instructions are provided in a separate document.

Video lectures?

Coming soon! :-)

Video lectures from a previous iteration of this course are available at:

• https://youtu.be/Z84uQhrNc70
• https://youtu.be/G6fb162N5ys

Expected workload

• 4x Session workbooks + exercises: 4x 4 hours
• 4x Assignments: 4x 4 hours
• 1x Project: 16 hours

Contact and feedback

For questions and feedback, please contact:
Dr. Sid Kumar
Assistant Professor
Delft University of Technology
[email protected]