Generative AI Pipeline for Material Design
Project Description
The project aims to explore the intersection of materials science and artificial intelligence. The project will focus on the application of generative AI algorithms for phase-transforming ferroelectric materials accompanied by the change of transport properties. The goal is to verify if the AI-based pipeline can accurately predict key properties of ferroelectric materials. This project will provide students with a unique opportunity to delve into the rapidly evolving field of AI in materials science and contribute to its advancement.
Supervisor
CHEN Sherry
Quota
3
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
The main task will be to the prompt engineering as a verification process to test the accuracy of the AI algorithm’s predictions. This will involve creating a dataset of ferroelectric materials with known properties, running the AI algorithm, and comparing the predicted results with the actual values. The project will conclude with an analysis of the results and recommendations for improving the AI algorithm.
Applicant's Learning Objectives
Students will need to understand the background knowledge of ferroelectric materials and the factors affecting their polarization and transformation temperature. Secondly, they will need to familiarize themselves with the Gen-AI based algorithm under python coding framework. They need to know how the artificial intelligence can be applied to materials science.
Complexity of the project
Challenging