Predictive Multiscale Materials Design

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

• 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.

Requirements

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. 

• 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

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:

• 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

“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