Implementation: Medium: Computational Materials Science Summer School - Fostering Accelerated Scientific Techniques (CMS3-FAST)

Our Mission Statement

To integrate CMS, ML/AI techniques, and AHPC into one comprehensive education and hands-on training program to drive transformative fundamental research in MSE. The program aims to introduce immersive visualization and materials informatics in CMS to K-12 students to promote and encourage their interest in pursuing higher education in related fields.

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Our Purpose

The purpose of this project is to address the knowledge gap in the workforce caused by the rapid advancements in Computational Materials Science (CMS), Machine Learning (ML)/Artificial Intelligence (AI) techniques, and Accelerated High-Performance Computing (AHPC). By integrating these three areas into a comprehensive education and hands-on training program, the project aims to drive transformative research and innovation in Materials Science and Engineering (MSE). It will utilize immersive visualization tools, such as Virtual and Augmented Reality (VR/AR), and AI-based tools to provide a tailored learning environment, helping to foster large-scale workforce development and encouraging interest in MSE from K-12 to higher education levels.

Our Goals

  • Integrate CMS, ML/AI techniques, and AHPC into one comprehensive education and hands-on training program.
  • Leverage immersive visualization through VR/AR tools and AI-based natural language generation tools to provide a tailored environment to participants with different backgrounds and learning styles.
  • Rigorously test and enhance the scaling of the education and hands-on training components, while also broadening access to enable large-scale and unrestricted workforce development.
  • Develop studio-based curricula that integrate CMS, ML/AI techniques, and AHPC at various levels of complexity for both undergraduate and graduate students.
  • Expand the current network of CI professionals and contributors to establish a powerful platform that accelerates advancements in MSE through continuous training and development.

Acknowledgements

We gratefully acknowledge support from the National Science Foundation and the following Texas A&M University facilities: Division of Civil, Mechanical and Manufacturing Innovation within the Directorate for Engineering.

This project is supported by NSF award number 2321005