Machine Learning for Materials

“In this course, you won't just watch AI in action, you'll run it yourself—on your own computer, in the cloud, with complete control.” – Professor Markus J. Buehler 

Rapid advances in machine learning and AI are revolutionizing the way materials are discovered, designed, and manufactured—helping researchers, scientists, and engineers create high-performing, cost-effective, and sustainable materials on an accelerated scale. Today, materials that previously took years to create can be produced in months or even weeks. This is the future of materials design—and you’re invited to learn on the cutting-edge at MIT.

Join renowned materials scientist Professor Markus J. Buehler for a four-day, live online course at the intersection of AI and materials design. Alongside accomplished peers from around the world, you’ll learn how to integrate the latest machine learning and AI technologies into your material design processes. With cutting-edge tools including DNA and protein models, graph neural networks, and computer vision, you’ll discover how AI can optimize materials design, analysis, and modeling. Whether you’re focused on developing alloys, metals, or biomaterials, these technologies open endless possibilities, no matter your field or industry.

What sets this program apart is its focus on demystifying AI. Rather than merely teaching you how to apply AI models, Professor Buehler will help you develop the critical skills you need to understand how these models work. The curriculum incorporates real-world case studies from a broad range of industries, including additive manufacturing, nanotechnology, and healthcare to provide essential, real-world context. You’ll gain hands-on experience with deep learning techniques such as convolutional neural nets, adversarial methods, and graph neural networks, along with symbolic and hybrid approaches for materials discovery. Emphasis will also be placed on working with a range of data types, including images, voxel data, dynamical data, and synthetic datasets, as well as utilizing visualization and analysis techniques like cluster analysis, statistical methods, and interpretable machine learning.

In the spirit of MIT’s motto “mens et manus” (mind and hand), this course combines theoretical learning with practical, interactive coding exercises, providing you with the experience you need to solve your own materials design problems. By mastering AI and machine learning techniques, you’ll learn to improve the speed, efficiency, and cost-effectiveness of your materials discovery and development workflows—giving you a competitive edge in today’s rapidly evolving innovation economy.

A standout feature of this program is the opportunity to create your own AI model from scratch. Professor Buehler will guide you through every step of the process—from engineering the model on your computer to running it on the cloud. Using your custom-built model, you’ll apply the AI skills you've developed to address your own materials design challenges—whether that’s discovering new molecules, improving existing material properties, or enhancing and optimizing your manufacturing processes.
 

What’s new for 2025?
This year, the course introduces exciting updates, including the integration of advanced data processing tools that significantly reduce the need for manual data curation. In the past, companies had to hire large teams to manually extract insights from unstructured data. Now, AI models can read and process vast amounts of unstructured data, such as handwritten reports, and turn them into actionable insights. Professor Buehler will show you how to leverage these leading-edge processing tools, so you can unlock new value from your legacy data.

The live virtual format of this program also means you can participate from anywhere in the world, engaging in real-time coding exercises and receiving dozens of code examples and data sets that you can immediately apply to your projects. This practical, hands-on approach will empower you to confidently navigate and utilize AI across your organization, whether you're building models or applying them to solve complex material design challenges. 

"By learning the fundamentals of AI, you can make informed decisions about which models to use and how to apply them strategically, avoiding the common pitfall of rushing into technology without understanding it."  – Professor Markus J. Buehler

• Incorporate advanced AI tools like GPT-3, AlphaFold, and graph neural networks into material design processes.
• Use deep learning models (e.g., convolutional neural networks [CNNs], transformers) to analyze and model materials across various data types.
• Optimize material discovery and development with neural interatomic potentials and multiscale modeling to predict and exploit physical properties at the nanoscale.
• Utilize symbolic hybrid methods and materiomic databases for efficient material development.
• Gain hands-on experience running AI models to solve real-world material design challenges.
• Apply techniques like cluster analysis and VR to interpret complex material data.
• Use AI to extract actionable insights from unstructured data, including handwritten reports.
• Create custom AI platforms that you can leverage across your organization using coding examples, templates, and datasets for real-world applications.
• Assess and adapt AI models to meet the specific needs of your projects and industry.
• Develop the skills to confidently build and apply AI models in materials design and development.

This course is ideal for professionals from a broad range of industries looking to design and manufacture next-generation materials—at speed and scale—for their organizations.

Professionals who would particularly benefit from the experience include:

• Software engineers or data scientists who want to leverage data to design and produce better materials. 
• Technology outreach directors, technology scouts, IP/patent professionals, or consultants who need to stay on the cutting-edge of intelligent material design.
• Sustainability directors who are looking for environmentally friendly alternatives to current materials. 
• Technical leaders or business intelligence managers/directors who need to make informed decisions related to material design strategies and investments. 
• Entrepreneurs, founders, investors, venture capitalists, futurists, and visionaries looking to stay abreast of new opportunities in material design. 
• Creatives and science marketers who need to understand the technologies and trends driving next-generation smart materials.
• Policymakers who want an overview of the challenges and opportunities in materials design across industries. 

Markus J. Buehler
Markus J. Buehler is the McAfee Professor of Engineering at MIT with positions in both the Center for Materials Science and Engineering and the Center for Computational Science and Engineering at the Schwarzman College of Computing. His research focuses on developing new modeling, design, and manufacturing approaches for advanced biomaterials with enhanced resilience and controllable properties across scales. His interests span a wide range of functional material properties—mechanical, optical, and biological—and explore the connection between chemical features, hierarchical structures, and performance in physiological, pathological, and extreme conditions. Utilizing molecular and multiscale modeling, design, and experimental methods, his work examines complex hierarchical materials, such as nanotubes, graphenes, and natural biomaterials like collagen, silk, and elastin. A pioneer in AI for science, Professor Buehler has significantly advanced the fields of materiomics and predictive materials design.