Revolutionizing Materials Informatics Education with nanoHUB: No-install, Browser-based Tools

In this seminar, I will present an innovative online platform for teaching materials informatics, designed to seamlessly integrate advanced computational tools with an accessible user experience. Leveraging nanoHUB's browser-based infrastructure, this resource allows students to run machine learning models, explore materials data, and engage with computational notebooks—all without the need for complex installations or dependency management. The course materials include topics from property prediction to advanced machine learning algorithms like graph neural networks and large language models, ensuring that students at various levels gain both foundational knowledge and cutting-edge insights. Additionally, a specialized module focuses on semiconductor-specific applications, offering practical experience in image segmentation for flaw detection, electronic property prediction, and multi-objective optimization. This resource represents a leap forward in democratizing access to computational education, making materials informatics more approachable for a broad audience. Join me as I explore how this platform can transform the way we teach and learn materials science.