Capstone Project

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Data and Materials Science: Capstone Project

Course Overview

In these special topics courses, Data Science and AI principles will be applied to a series of materials science projects. Interdisciplinary research projects will solve real-life problems.

  • Students will attend a series of relevant seminars at the intersection of data science, machine learning, and materials science.

  • Students will work in interdisciplinary teams under the direction of their project mentor/faculty advisor to research, develop, and carry out a project incorporating elements from machine learning and materials science.

This course is designed for advanced graduate students with a background in data science, machine learning, and materials science. This course is one of four courses that make up the AI for Materials Science (aiM) certificate (in development).

To ensure students complete their projects successfully, they will attend relevant seminars and complete assignments throughout both semesters that provide guidance, practice, and feedback about students’ teamwork, project management/communication plan, and overall progress in relation to the project.

The proposal, final project paper, and presentations will be evaluated by the aiM faculty team and relevant outside stakeholders on multiple dimensions including students’ ability to communicate effectively to a diverse audience, strategy, and creativity.

Learning Objectives 

  • Describe current research in the fields of Data Science and Materials Science.
  • Draw connections between current research in the fields of Data Science and Materials Science to their own research projects.
  • Conduct a literature search and literature review to help develop their project idea.
  • Apply team science and project management strategies in the planning and execution of projects.
  • Design moderately sophisticated projects in the intersection of data science and materials science.
  • Execute computational and/or experimental projects.
  • Communicate their plan, process, and project results through written, oral, and visual methods.


Students participating in the aiM program are eligible for this course. Applications of Data and Materials Science should be taken prior to this course.

Ongoing Research Projects


Complex dynamic systems research project
Model-form uncertainty quantification on complex dynamic systems in a data-driven approach. Yixin Tan, Hao Zhang; Advisor: Johann Guilleminot & Jianfeng Lu


Interoperable variational autoencoders research project
A Reduced Order Model on Nonlinear Manifolds using Interpretable Variational Autoencoders. Peiyi Chen & Gary Hu; Advisor: Johann Guilleminot


Metamaterials research project
Engineering Band Gaps in 3D Printable Metamaterials Using Interpretable Machine Learning. Mary Bastawrous & Zhi Chen; Advisors: Cate Brinson & Cynthia Rudin