Join me on a journey into the cutting-edge realm of Machine Learning (ML) in Materials Science, progressing from basics to hands-on use of large language models and on-line data bases. ML and Artificial Intelligence (AI) have become the defining features of our era, revolutionizing domains from medicine to autonomous driving. Over the past decade, ML has rapidly advanced, dominating in silico applications such as image analysis, recommender systems, and the development of large language models. Now, envision the forthcoming decade as the era of ML's impact on real-world (you can say “material”) challenges. With ML, we can unlock new frontiers in materials and process optimization, pave the way for groundbreaking materials discovery, and even delve into the intricacies of physics at the nanoscale. This course is designed to equip you with the fundamental knowledge of principles and underpinnings of ML methods while delving deep into the realm of practical applications from lab to the fab. The instructor worked on ML in materials science for 15 years at ORNL and spent a year at Amazon (special projects). By enrolling in this course, you will embark on an exciting exploration of ML's potential in solving real-world scenarios. Discover how ML can revolutionize Materials Science, unraveling complex problems and uncovering innovative solutions. Join today and be at the forefront of this transformative field, where the convergence of ML and Materials Science promises a future brimming with limitless possibilities.
Why This Course is a Must-Join:
• Hands-On Machine Learning (ML) Experience: Apply ML and AI to real-world problems in materials science, from microscopy to physical characterization
• Comprehensive Learning: Start with basics like Principal Component Analysis and advance to cutting-edge Large Language Models.
• Industry Insights: Discover how tech giants like Amazon, Google and Meta as well as leading chemical and materials companies are harnessing ML for their R&D and products.
• Future-Ready Skills: Be a part of the transition of ML from in-silico applications to real-world materials and device innovation, from cloudified tools to automated laboratories
• Problem-Solving Approach: Learn to work backwards from real-world problems to solutions, a crucial skill for your future career.
Who Should Attend?
• Undergraduates: Gain an edge for industrial roles or graduate studies by mastering ML early
• Graduates: Embark on an AI journey in the materials world and stand out in your field
Join us for an exciting exploration of ML's transformative potential in Materials Science. Unravel complex problems and uncover innovative solutions.
Contact: Sergei V. Kalinin – [email protected]