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.
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