AI for Next-Generation Semiconductor Material Discovery

This workshop explores AI and ML applications in discovering next-gen semiconductor materials like GaN, SiC, and 2D materials. Participants will gain hands-on experience in data extraction, property prediction, and deep learning for material discovery.

The workshop aims to teach AI and ML techniques for predicting properties of semiconductor materials like GaN, SiC, and 2D materials, focusing on data extraction, property prediction, and deep learning applications.

This workshop is intended for researchers, engineers, and students in materials science, semiconductor technology, and AI/ML. It is ideal for those interested in applying AI techniques to material discovery and property prediction in the semiconductor industry. Prior knowledge of Python and basic machine learning concepts is recommended but not required.

• Proficiency in AI/ML Techniques: Participants will gain the skills to apply AI and ML algorithms for predicting material properties in semiconductor research.

• Hands-on Experience: Practical experience in data extraction, preparation, and machine learning model development.

• Understanding Advanced AI Tools: Familiarity with deep learning models like CGCNN, MEGNet, and ALIGNN for semiconductor material discovery.

• Enhanced Research Skills: Ability to evaluate and interpret machine learning models for material property prediction.

• Career Readiness: Equipped with the knowledge and skills to pursue careers in materials science, AI research, and semiconductor technology.