AI-for-Science: Bridging the Network Learning and Functional Properties of Metal Nano-Composites
Learn how AI-driven network models can accelerate the design of next-generation metal nanocomposites with tunable functional properties.
Project description
This project explores how artificial intelligence and network-based learning can be used to understand and predict the functional properties of metal matrix nanocomposites, including thermal behavior, corrosion resistance, tribological performance, and surface wettability. Metal nanocomposites possess complex multiscale structures, where nanoscale features (such as particle distribution, interfaces, and electron interactions) strongly influence macroscopic material performance. In this project, students will represent these complex material systems as networks or graphs and apply modern machine learning methods (e.g., graph neural networks, representation learning, and data-driven modeling) to uncover relationships between structure, electron behavior, and material properties.
Project outcome
Students will develop practical skills in machine learning and graph-based learning by building AI models that represent metal nanocomposites as networks and predict their functional properties. Participants will gain hands-on experience with data processing, model training, and interpretation of AI results, while learning how nanoscale material structures and electron behavior influence real-world performance. The project will strengthen students’ ability to apply computational and mathematical tools to materials science problems and prepare them for advanced study or careers in AI, data science, and materials engineering.
Project details
| Length of commitment | Longer than a semester (about 6-9 months) |
| Start time | Anytime |
| In-person, remote, or hybrid? | Hybrid |
| Level of collaboration | Small group project (2-3 students) |
| Benefits | Potential academic credit |
| Who is eligible | Juniors Seniors |
Core partners
- Shuaihang Pan, Assistant Professor, Department of Mechanical Engineering, University of Utah.
Project mentor
Xudong Fan
Assistant Professor
Civil, Structural, and Environmental Engineering
Phone: (716) 645-2298
Email: xudongfa@buffalo.edu
Start the project
- Email the project mentor using the contact information above to express your interest and get approval to work on the project. (Here are helpful tips on how to contact a project mentor.)
- After you receive approval from the mentor to start this project, click the button to start the digital badge. (Learn more about ELN's digital badge options.)
Preparation activities
Once you begin the digital badge series, you will have access to all the necessary activities and instructions. Your mentor has indicated they would like you to also complete the specific preparation activities below. After you’re approved to begin the project, your mentor will send the relevant materials. Please reference this when you get to Step 2 of the Preparation Phase.
- Familiarizing with basic Python Programming.
- Familiarizing with basic image processing and machine learning algorithms (ideally in graph learning and network science).
- Familiarizing with metal properties via reading reference 1.
Keywords
computer science, engineering, mathematics, material science, AI, machine learning, science