Project 7: Computational Design of Low-Cost/High-Efficiency Solar Cell

This project explores organic dye-sensitized solar cells (ODSSCs) as a low-cost and flexible clean energy technology.

Project Summary

This project introduces participants to the intersection of High-Performance Computing (HPC), Artificial Intelligence (AI), and renewable energy through hands-on analysis of solar cell data. Participants will explore real-world solar cell datasets, leveraging HPC tools and AI techniques to extract meaningful insights. The project bridges theoretical concepts with practical applications, empowering students to tackle energy-related challenges using cutting-edge computational methods.

Learning Objectives

  1. Introduce HPC Fundamentals: Familiarize students with HPC concepts, including parallel computing and large-scale data processing, using ALCF resources.
  2. Explore AI Applications: Demonstrate how machine learning can be applied to analyze solar cell performance and predict efficiency.
  3. Analyze Solar Cell Data: Provide hands-on experience with solar cell datasets to understand material properties and energy output. 

Project Lead

Peer Mentor