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Markus J. Buehler's picture

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

In this course you will fully learn how to incorporate new materials informatics 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.

Mogadalai Gururajan's picture

The SIAM 100-digit challenge of Bronemann et al: A review

Suppose if somebody asked you the following question, and more importantly, wanted the answer to an accuracy of 100-digits:

  • Problem A: A particle at the center of a 10 x 1 rectangle undergoes Brownian motion (i.e., two-dimensional random walk with infinitesimal step lengths) until it hits the boundary. What is the probability that it hits at one of the ends rather than at one of the sides?

Or, this question (again, demanding the answer to an accuracy of 100-digits):

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