Open House: Machine Learning for Materials Informatics
Machine learning-assisted computational modeling and simulations are speeding the pace at which engineers are developing new materials. Intelligent materials design not only creates better and stronger materials but can do so more cost-efficiently and with a more sustainable usage of resources and unconventional materials. It is essential that engineers are familiar with the fundamentals of machine learning tools to keep pace with the advancements in materials science.
Machine Learning for Materials Informatics is a four-day, live virtual course taught by MIT Professor Markus J. Buehler. Participants will learn how to speed their design process and streamline prototyping in their organization, from the molecular to the macro level. Professor Buehler will introduce the foundations of materials informatics and lead a variety of labs using tools like Pytorch and TensorFlow. By the end of the course, learners will have the codes and templates they need to build their own AI platforms.
At this virtual open house, you will:
- Meet Markus J. Buehler, Jerry McAfee (1940) Professor of Engineering at MIT
- Explore the learning outcomes, such as how to build predictive models, solve inverse design problems with AI, shape general-purpose models for materials applications, and more
- Envision how this curriculum, the interactive labs, and the accomplished peers you network with over the four days can inspire new ways of approaching your work challenges
- Ask your curriculum questions during a live Q&A session