Predictive Models Optimizing Doping Profiles and Layer Compositions for Enhanced Light Absorption
The aim of this workshop is to teach participants how to use AI techniques to optimize solar cell efficiency through bandgap engineering, doping profiles, and layer compositions.
To understand the role of AI in optimizing solar cell performance.
To explore machine learning techniques for bandgap prediction and doping optimization.
To gain hands-on experience with solar cell data and AI tools.
To learn how to apply deep learning models for solar cell efficiency enhancement.
To develop skills in using Python, TensorFlow, and Scikit-learn for solar energy applications.
Proficiency in AI techniques for optimizing solar cell performance.
Skills in bandgap engineering, doping profiles, and layer composition optimization.
Experience with Python, TensorFlow, and Scikit-learn for solar applications.
Hands-on practice with open solar datasets.
Understanding of deep learning models for solar energy research.
Researchers and professionals in renewable energy, material science, and AI
Students with an interest in solar energy and AI applications
Individuals seeking to apply AI techniques to optimize solar cell performance
Those with a basic understanding of machine learning, Python, and solar cell technologies
