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Lead Instructor(s)
Jun 03 - 07, 2024
Registration Deadline
On Campus
Course Length
5 Days
Course Fee
3.0 CEUs
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Join the cutting-edge of intelligent material design and discover how to integrate advanced technologies to drive the development of next-generation smart materials. In this condensed five-day course, you will participate in hands-on clinics and labs designed to help you optimize your smart material design and manufacturing through the use of large-scale computational modeling, material informatics, and artificial intelligence / machine learning. 

This course may be taken individually or as part of the Professional Certificate Program in Design & Manufacturing or the Professional Certificate Program in Innovation & Technology.

Course Overview

Computational methods like molecular modeling and AI/ML are revolutionizing the materials design world. Today, an engineer or scientist can simply enter the desired properties into a computer program and the system will manufacture a microstructure with those specifications. Algorithms can be used to predict which chemical building blocks can be combined to create advanced materials with superior functions—from ultra-strong, lightweight materials used in the automotive, construction, and aerospace industries, to biomaterials used in implants and biomedical devices with the ability to self-heal and regenerate.

In this course, you will enhance your ability to leverage materials design, machine learning, and additive manufacturing to create better materials, with emphasis on four of the most in-demand areas of materials engineering:

  • Computational modeling: Molecular dynamics, multiscale methods, and high-throughput experimental data collection and analysis
  • Biomaterials and bio-inspiration: Proteins, natural composites, smart and tunable materials, and biomass engineering
  • Machine learning & computing: Material informatics and AI/ML applications in materials modeling, design and manufacturing, and data set generation
  • Additive manufacturing: Manufacturing designer materials, e.g. composites

Alongside peers from around the world, you will gain insights into the science, technology, and state-of-the-art computing methods being used to fabricate innovative materials from the molecular scale upwards. Through lectures and hands-on labs and clinics, you will learn how to construct, in a bottom-up manner, atomically precise products through the use of molecular design, predictive modeling, and manufacturing, allowing the fabrication of a vast array of advanced, innovative designs for a wide-range of applications. You will also learn how to access and utilize web-based machine learning tools for materials analysis, and cement your knowledge with a “from design to production” project, in which you will use AI and other computational methods to produce a custom 3D-printed smart material.

Office hour sessions will be held several weeks after the program, providing the opportunity to ask in-depth questions after you have had a chance to reflect on the course material.

Certificate of Completion from MIT Professional Education

Predictive Multiscale cert image
Who Should Attend
  • Lead scientists or engineers who work in fields that require advanced materials design, development, or manufacturing skills.
  • 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 communicators/marketers who need to understand the technologies and trends driving next-generation smart materials. 
  • Policymakers/influencers who want an overview of the challenges and opportunities in materials design across industries.  

The course is additionally beneficial to anyone working in materials or an industry that builds on a material interaction platform (such as pharmaceuticals, regenerative medicine, energy, or materials engineering) who is interested in understanding how to optimize a material’s structure and performance.


A computer with internet access is required for this course. Computing requirements will be taught using browser-based cloud computing accessible via laptops. No coding experience is needed (web-based tools will be used and web-based machine learning notebooks provided).  

Participants will be expected to review a carefully curated collection of readings and videos in preparation for the course. These materials will help maximize your experience. You will also have the chance to complete a pre-course survey to help the instructors identify common interests and challenges participants want to solve in the machine learning clinic. 


"I can't think of an area where this course didn't exceed my expectations and I would almost love to take it again."
"The content is very relevant. The examples very chosen and explained. Professor Buehler is a gifted teacher. The labs were very lively and enabling to set the theoretical material in the mind. A great course."
"An intoxicatingly comprehensive course. It will take weeks to unpack, savor, and apply the techniques taught."
"I also really appreciated the detail that Dr. Buehler went into on each slide. He documented on the slides the key points that he discussed during his lecture."
"Markus Buehler is extremely knowledgeable, and was able to address questions from a very varied audience."
“Markus Buehler is nothing but supportive, attentive, and patient with the class. I’ve learned so much about artificial intelligence and machine learning.”
Reddhy Mahle
Learning Outcomes
  • Master cutting-edge computational tools that range from multi-scale modeling to machine learning and artificial intelligence. In labs and clinics, you will: 
    • Design and carry out multiscale modeling
    • Embed the models into AI systems using state-of-the-art deep learning approaches
    • Explore experimental techniques that probe, understand, and can be used to design the ultimate structure of materials—from atoms upwards
  • Use design tools to predict mechanical properties—such as strength, toughness, deformability, and elasticity, as well as optical, thermal, and electronic properties—and learn to predict material failure of 3D printed designs
  • Utilize cloud computing to design novel designer proteins and superior material properties by mimicking bio-inspired materials found in nature and biology
  • Synthesize computationally designed hierarchical composites using 3D printing and other advanced manufacturing techniques, followed by subsequent mechanical testing 
  • Design and generate novel materials in a computational-manufacturing pipeline
  • Evaluate and apply computational tools in materials design (synthesis and testing), including those related to molecular mechanics, nanotechnology, multiscale and hierarchical materials, and emerging materials technologies
  • Explore fundamentals and codes for performing state-of-the-art techniques—such as molecular dynamics, molecular mechanics, and coarse-graining—used to cover a range of length- and time-scales
  • Receive instructor feedback on your real-world challenges during a hands-on clinic

This course runs 9:00 am - 5:00 pm each day except for Friday when it ends at 1:00 pm. There is a networking reception on the evening of the first day. 

Day 1

  • Basic methods and applications in computational materials science 
  • Introduction to materials informatics and AI/ML
  • Introduction to machine learning clinic: First steps
  • Participant reception and networking

Day 2

  • 3D printing lab
  • Step-by-step in-class design studio
  • Additive manufacturing of multi-material optimized materials
  • Molecular modeling, design, and data visualization lab
  • Interactive case studies and participant presentations
  • Optional group work time

Day 3

  • Advanced modeling methods
  • Advanced machine learning methods (featuring autoencoders, NLP, transformer, game theory/GANs, graph neural networks and geometric deep learning)
  • Machine learning clinic (part I) and hands-on learning exercises
  • Interactive design (VR/AR, data visualization) and materials processing lab
  • Optional group work time

Day 4

  • Extreme material performance (failure case study)
  • Experimental data collection, high-throughput approaches, and dataset curation
  • Category theory model demonstration and bio-transfer
  • Teamwork group labs and assignment presentation
  • Continuation of machine learning clinic (part II)

Day 5

  • Computational-experimental methods (cloud computing, neural modeling, Bayesian process optimization)
  • Advanced computational methods (GPU computing, quantum computing, neuromorphic computing)
  • Big data and analytics
  • Select design topics
  • Custom 3D printed smart material case study outcome
  • Course review and certificate ceremonyLinks & Resources

Links and Resources



“Markus Buehler is nothing but supportive, attentive, and patient with the class. I’ve learned so much about artificial intelligence and machine learning.”

- Reddhy Mahle

Reddhy Mahle Headshot

Download the Course Brochure
Predictive Multiscale Materials Design - Brochure Image


The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.

Fundamentals: Core concepts, understandings, and tools - 40%|Latest Developments: Recent advances and future trends - 40%|Industry Applications: Linking theory and real-world - 20%
Delivery Methods

How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.

Lecture: Delivery of material in a lecture format - 70%|Discussion or Groupwork: Participatory learning - 15%|Labs: Demonstrations, experiments, simulations - 15%

What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.

Introductory: Appropriate for a general audience - 80%|Specialized: Assumes experience in practice area or field - 15%|Advanced: In-depth explorations at the graduate level - 5%