AI-Driven Bandgap Engineering for Efficient Solar Cells

Introduction to the Course

Course Objectives

• Understand the importance of bandgap engineering in solar cell performance and energy conversion.
• Learn how AI and machine learning models can predict and optimize bandgap for semiconductor materials.
• Gain hands-on experience in material property prediction, modeling, and data analysis for solar cell applications.
• Master computational tools and algorithms to design novel materials with tailored bandgaps for enhanced solar cell efficiency.
• Explore advanced topics such as perovskite solar cells, multi-junction solar cells, and hybrid materials for next-gen solar technologies.
• Develop the ability to apply AI methods to real-world solar cell design challenges and optimization workflows.

What Will You Learn (Modules)

Module 1 – Introduction to AI in Solar Cell Optimization

• Understanding the Role of Bandgap Engineering in Solar Cells
• Recent Advances in Solar Cell Technologies
• Overview of AI and Machine Learning in Solar Energy Research

Module 2 – Machine Learning for Bandgap Prediction and Optimization

• Machine Learning Techniques for Bandgap Prediction
• Discover key descriptors used for doping profiles and layer compositions.
• Build and train predictive models to optimize bandgap properties for solar cells.

Module 3 – Deep Learning and Advanced AI for Solar Cell Efficiency

• Deep Learning Models for Solar Cell Property Prediction
• AI Tools for Doping Optimization in Perovskite and Silicon Solar Cells
• Implement pretrained deep learning models for bandgap engineering in solar cells.

Who Should Take This Course?

This course is ideal for:

• Bioinformaticians and computational biologists working with genomic or transcriptomic data
• Genomics researchers focusing on RNA-Seq or gene expression analysis
• Biologists, biochemists, and geneticists who wish to gain computational tools for RNA-Seq data analysis
• Students in bioinformatics, genomics, or systems biology programs

Job Opportunities

After completing this course, learners can pursue roles such as:

• Bioinformatics Analyst (RNA-Seq)
• Computational Biologist
• Genomics Researcher
• Transcriptomics Specialist

Why Learn With Nanoschool?

At NanoSchool, we focus on career-relevant learning that builds real capability—not just theory.

• Expert-led training: Learn from instructors with real-world experience in applying skills to industry and research problems.
• Practical & hands-on approach: Build skills through guided activities, templates, and task-based learning you can apply immediately.
• Industry-aligned curriculum: Course content is designed around current tools, workflows, and expectations from employers.
• Portfolio-ready outcomes: Create outputs you can showcase in interviews, academic profiles, proposals, or real work.
• Learner support: Get structured guidance, clear learning paths, and support to stay consistent and finish strong.

Key outcomes of the course

Upon completion, learners will be able to:

• In-depth knowledge of bandgap engineering and its effects on solar cell efficiency
• Skill to utilize AI algorithms to predict and optimize bandgap properties in semiconductor materials
• Practical experience in perovskite and multi-junction solar cells, as well as other advanced materials
• Skill to design and analyze solar cells with optimized bandgap arrangements for enhanced energy conversion
• Project idea ready for portfolio submission on AI-optimized solar cells