Machine Learning for Materials Informatics

“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

NOTE: This program is designed to complement the on-campus course Predictive Multiscale Materials Design, but can also be completed independently.

Certificate of Completion from MIT Professional Education

• 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.

PROGRAM OUTLINE

NOTE: All times Eastern Daylight Time (UTC-4:00). A few introductory lecture videos will be posted ahead of the course.

Day One

9am-noon: Foundations in material informatics (data science and basic concepts of machine learning; multiscale modeling; datasets, experimental methods for data collection)

1-2pm: Clinic #1: Convolutional neural network (classifier, regression, and peeking inside via interpretable methods)  

2-4pm: Digging deeper: Deep neural nets, loss functions, Stochastic optimization methods (e.g., stochastic gradient descent and variants), Regularization

4-5pm: Clinic #2: Material failure analysis

5-7pm: Interactive virtual networking reception (get to know peers, the instructor, and make connections)

Day Two

9-10am: Hands-on introduction to PyTorch (example application to fine-tuning a BERT NLP model applied to proteins)

10-11am: Hands-on introduction to TensorFlow (example application to developing an adversarial neural network)

11am-noon: Practical guide to tensor algebra and other important math concepts needed

1-2pm: Ethics, bias and sustainability in material informatics

2-3:30pm: Data, data, everywhere…De novo dataset construction (imaging lab) and application to build a deep neural network (covers computer vision tools, live imaging using depth camera

3:30-5pm: Introduction to graph neural networks (applications to molecular systems, truss systems, alloys, proteins, and healthcare; graph transformers)

Day Three

9-10:30am: Transforming AI and healthcare with attention (AlphaFold and applications to protein design, synthesis)

10:30am-noon: Deepening the understanding of language models applied to materials (pre-training and fine-tuning); BERT and GPT-3-like (applications of large language models to materials problems; category theory; time-dependent material phenomena)

1-2pm: Clinic #3: Transformer models for inverse materials design (develop multiscale transformer model from scratch)

2-3pm: Adversarial neural networks and applications to materials design (manufacturing, inverse problem, characterization)

4-5pm: Case study: Image segmentation in microscopy, medical imaging, and analysis

Day Four

9-10am: Autoencoders (vision, graphs, NLP, proteins)

10-11am: Clinic #4: To fail or not to fail: Buckling modeling (time-dependent phenomena)

11am-noon: Concluding discussion

Noon-12:30pm: Graduation ceremony and certificates

Post-course: Participants will have the opportunity to ask additional questions at two office hour sessions, which will be offered one and two weeks after the conclusion of the course.

Links and Resources

Video/Audio:

• Generative AI and Its Business Impact
• Engineered Spider Silk - Taking Cues from Biological Materials
• Examining Failure to Test Limits of Materials Function
• Materials Simulation Through Computation and Predictive Models
• Using Computation to Validate Predictability of Materials Models

News/Articles:

• Revolutionizing Materials Science: An Interview with Markus Buehler on the Impact of AI
  In this interview, Markus Buehler discusses MIT's Machine Learning for Materials Informatics course, its curriculum, and its impact on the broader materials science community, emphasizing the exciting potential for AI-driven discoveries in sustainable materials and energy solutions.
• How Generative AI Is Transforming Materials Design
  • As Professor Markus Buehler explains, Generative AI is fundamentally changing materials design, not only making materials scientists faster but giving them capabilities they've never had before.
• Six keys to generative AI success
  • Generative artificial intelligence is upending our previous assumptions about how — and how quickly — new materials can be designed, with algorithms already helping companies and researchers to automate performance forecasting, improve existing materials, and even generate arrays of potential designs for new materials that meet specific criteria.
• What Industry Can Learn from Nature About Product Design
  • In their quest to make materials stronger and more sustainable, designers should look to the lessons offered by spiderwebs and seashells.
• MIT Materials Scientist Markus J. Buehler on Nanotech, Machine Learning, and Professional Education
  • MassTLC spoke with Professor Buehler about the machine learning course, what advances in materials research mean for sustainable design, and why professional education matters.
• School of Engineering third quarter 2021 awards
  • Markus Buehler of the Department of Civil and Environmental Engineering won the Daniel C. Drucker Medal on June 21.
• Considering the spiderweb
  • After nearly a decade, an interdisciplinary collaboration to model a 3D spider web leads to many surprising results.
• A material difference
• Finding the love hormone in a stressed-out world
  • A new art/science collaboration uses molecular structures as its creative medium.
• There’s a symphony in the antibody protein the body makes to neutralize the coronavirus
  • Professor Markus Buehler composed it, and a South Korean orchestra performed it; it’s the latest in a series of artistic collaborations sparked by Buehler’s exploration of the structure of SARS-CoV-2.
• New AI tool calculates materials’ stress and strain based on photos
• Between spider webs, 3D printing, geckos and AI - a week at MIT's online campus
  • 2020 course participant reflects on his experience
• Marshaling artificial intelligence in the fight against Covid-19
  • The MIT-IBM Watson AI Lab is funding 10 research projects aimed at addressing the health and economic consequences of the pandemic.
• Translating proteins into music, and back
  • By turning molecular structures into sounds, researchers gain insight into protein structures and create new variations.
• How AI and 3D Printing are Revolutionizing Materials Design
• How to build better silk
  • Reconstituted silk can be several times stronger than the natural fiber and made in different forms
• Conch shells spill the secret to their toughness
  • Three-tiered structure of these impact-resistant shells could inspire better helmets, body armor.
• Jell-O jawed marine worm inspires MIT-developed material
• Researchers design one of the strongest, lightest materials known
  • Porous, 3-D forms of graphene developed at MIT can be 10 times as strong as steel but much lighter
• How to power up graphene implants without frying cells
  • New analysis finds way to safely conduct heat from graphene to biological tissues
• Finding a new formula for concrete
  • Researchers look to bones and shells as blueprints for stronger, more durable concrete.
• MIT Professor Merges Biology And Materials Through Biomateriomics
• Just hanging on: Why mussels are so good at it 
  • Understanding the strength of the shellfish’s underwater attachments could enable better glues and biomedical interfaces.
• Printing artificial bone
  • Researchers develop method to design synthetic materials and quickly turn the design into reality using computer optimization and 3-D printing.
• Decoding the structure of bone
  • MIT researchers decipher the molecular basis of bone’s remarkable strength and resiliency; work could lead to new treatments and materials.
• The music of the silks
  • Researchers synthesize a new kind of silk fiber — and find that music can help fine-tune the material’s properties.
• Seeing the music in nature 
  • From spider webs to tangled proteins, Markus Buehler finds the connections between mathematics, molecules, and materials.
• Envisioning Silk Stronger Than Steel

• 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. 

   

REQUIREMENTS

A computer with cloud computing access is required.