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MIT Short Course: Machine Learning for Materials Informatics (Live Virtual, Sept. 26-29, 2022)

Markus J. Buehler's picture

Machine Learning for Materials Informatics

Instructor: Prof. Markus J Buehler, mbuehler@MIT.EDU

Sept. 26-29, 2022 (3.5 days), Live Virtual 

Learn more here:

Special fellowships are available for postdocs and graduate students that cover part of the course fee; candidates are asked to contact mbuehler@MIT.EDU with a brief CV/biosketch for consideration.  

With the success of effective and generalizable deep learning tools, the materials community is primed to take advantage of unprecedented breakthroughs, leveraging materials modeling, analysis, and design toward a more efficient, less costly, and more versatile response to market demands and opportunities. This course will teach you how to tap into your existing data and develop an actionable vision for incorporating material informatics into current research strategies for developing technologies, services and new investigation directions. Moreover, with data available from autonomous experimentation or large databases like the Materials Genome initiative, there exist many opportunities to accelerate and expand your materiomic design platform.

In this course you will fully learn how to incorporate these new technologies and methods into your own material modeling, analysis and design processes in order to capitalize on recent AI breakthroughs, such as language models (e.g. GPT-3, BERT, LaMDA, etc.), DNA and protein models (e.g., AlphaFold), graph neural networks applied from molecular to macroscale structures, and a host of methods adapted for computer vision including diffusion models (as used in DALL-E 2 or Imagen), specifically for the analysis, design and modeling of materials. The course involves a mix of lectures, hands-on labs and clinics for an immersive experience. Participants will learn fundamentals and techniques to deploy machine learning in materials development and gain first-hand understanding of state-of-the art tools for varied applications ranging from data mining to inverse design. We will cover scales from the molecular to the continuum.

Specific topics covered:

  • Modern and cutting-edge machine learning tools, especially focused on deep learning (includes: convolutional neural nets, adversarial methods, graph neural nets, transformer models, diffusion models; neural molecular dynamics)
  • Analysis of images, voxel data, dynamical data, and graphs, as well as language and symbolic methods and hybrid approaches 
  • Visualization and data analysis methods, including statistical methods, graphic rendering, virtual reality

The instructor will masterfully break down this complex field into easy-to-digest concepts, to offer you direct access to leverage the new tools for your problem space, and to develop the skill to judge and assess the best tools for the job. Alongside peers from around the world, you will engage in interactive lectures and hands-on coding clinics and labs delivered in a live virtual format. These activities are designed to help you learn, design, and apply modern material informatics tools—specifically artificial intelligence and machine learning—including neural interatomic potentials, large-scale multiscale modeling to improve the speed, efficiency, and cost effectiveness of your discovery, prototyping, and development processes. You will learn how modern computational tools enable us achieve almost any desirable accuracy in multiscale material discovery, connecting quantum to the macro-world.

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