Course is closed
Lead Instructor(s)
Jun 21 - 25, 2021
Registration Deadline
Live Virtual
Course Length
5 Days
Course Fee
3.0 CEUs
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Be on the strategic front of intelligent material design; learn how to integrate the most advanced technologies to drive the development of next generation’s smart materials. In this condensed five-day course, you will learn how to use advanced methods of large-scale computational modeling, material informatics, and artificial intelligence/machine learning to optimize and leverage your smart material design and manufacturing.

There are no prerequisites for this course. You will join accomplished peer participants to learn how to access web-based machine learning tools for materials analysis, culminating their knowledge gained with a “from design to production” project at the end of this high-paced 5-day course, using AI and other computational methods to produce a custom 3D printed smart material.

Course Overview

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.

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 program and the system will manufacture a microstructure that matches the specifications. Algorithms 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.

Get exposure to four of the hottest areas in materials engineering, transforming many industries as designer materials emerge in practice:

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

This course covers the science, technology, and state-of-the-art computing methods being used to fabricate innovative materials from the molecular scale upwards, as well as advanced manufacturing methods, such as additive manufacturing. Through lectures and hands-on labs, participants 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.

Participant Takeaways

  • Learn cutting-edge computational tools that range from multi-scale modeling to machine learning and Artificial Intelligence: You will design and carry out multiscale modelling, and embed them in into AI systems. Explore experimental techniques that probe, understand, and can be used design the ultimate structure of materials—from atoms upwards.
  • Identify design tools to predict mechanical properties such as strength, toughness, deformability, and elasticity, as well as optical, thermal, and electronic properties. During the course, you will 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: You will design and generate novel material designs in a computational-manufacturing pipeline
  • Critically evaluate and apply the use of computational tools in materials design (synthesis and testing) – molecular mechanics, nanotechnology, multiscale and hierarchical materials, and emerging materials technologies
  • Access fundamentals and codes necessary to perform state-of-the-art techniques, such as molecular dynamics, molecular mechanics, and coarse-graining, used to cover a range of length- and time-scales.

Who Should Attend

  • Lead Scientists or Engineers, in fields requiring advanced materials design, development, or manufacturing
  • Technical VP/Manager, Future Ventures
  • Director, Business Intelligence
  • Founders
  • Software engineers
  • Data scientists
  • Policy makers and trendsetters
  • Futurists
  • Investors and venture capitalists
  • Technology scouts, IP/patent experts

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


There are no prerequisites for the 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). Course features group work. A computer with internet access is required for this live virtual course.

Program Outline

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

Day 1

Lectures covering theoretical, applied and hands-on experimental concepts and systematically learn the basic methods in the emerging field of computational materials science and how it is used to understand, design and manufacture new materials and structures. Introduction to materials informatics, AI/ML. Participant reception and networking.

Day 2

Remote virtual 3D printing lab, step-by-step in-class design studio and additive manufacturing of multimaterial optimized materials. Molecular modeling, design and data visualization lab. Interactive case studies and participant presentations. Optional group work time.

Day 3

Advanced modeling methods, quantum and advanced machine learning methods (feat.: autoencoders, NLP, GANs, graph neural networks). Machine learning lab and hands-on learning exercises. Lab on interactive design (VR/AR), materials processing. Optional group work time.

Day 4

Extreme material performance (failure case study). Experimental data collection and high-throughput approaches, dataset curation. Category theory model demonstration and bio-transfer. Teamwork group labs, and assignment presentation.

Day 5

Computational-experimental methods (cloud computing, neural modeling, Bayesian process optimization), advanced computational methods (quantum computing, neuromorphic approaches), big data and analytics, design. Custom 3D printed smart material case study outcome. Course review and graduation ceremony.

Links & Resources




"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."

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%
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