Computational Materials Science Summer School
Fostering Accelerated Scientific Techniques


July 7 – July 19, 2024

Application to 2024 CMS3-FAST is now closed.

In Person at Texas A&M University Campus in College Station, TX, and Remotely Online

Overview: The integration of Computational Materials Science (CMS) and Artificial Intelligence (AI)/Machine Learning (ML) techniques, along with Accelerated High-Performance Computing (AHPC) achieved using modern hardware accelerators such as Graphics Processing Units (GPUs), can provide a powerful platform for researchers to accelerate the advancements in Materials Science and Engineering. However, the rapid advancement of these fields has also created a knowledge gap in the workforce, with a shortage of professionals who are simultaneously trained in all three areas: CMS, AI/ML, and AHPC. The 2024 CMS3-FAST program is a beyond-state-of-the-art workforce development initiative that will integrate CMS, AHPC, AI/ML techniques, and Immersive Visualization through Virtual and Augmented Reality (VR/AR) tools into one comprehensive education and hands-on training program to drive transformative fundamental research in Materials Science and Engineering. The 2024 CMS3-FAST builds upon the success of our twelve-year legacy CMS3 program.

Why Attend CMS3-FAST:

  • Relevance: By integrating AHPC and AI/ML with CMS, participants will better understand the relevance of these techniques and how they can be applied to solve Materials Science and Engineering problems. This approach creates a more engaging and meaningful learning experience, increasing motivation and improving retention.
  • Contextual understanding: By contextualizing AI/ML and AHPC within CMS, participants can see the bigger picture and understand how the knowledge they gain fits into the broader field. This broader understanding will help them better appreciate the interdependence of these areas and improve their grasp of all three.
  • Fosters interdisciplinary thinking: By teaching CMS, AI/ML, and AHPC in an integrated manner, we will encourage interdisciplinary thinking and provide participants with a more complete understanding of the benefits and limitations of each area. This will lead to greater collaboration, insights, and discoveries in materials research, as well as the development of new tools and techniques.
  • Immersive visualization: Immersive visualization is increasingly vital in materials research, where complex data and models are common. VR/AR tools facilitate immersive experiences, improving understanding, revealing hidden patterns, and fostering creativity, leading to valuable insights and discoveries.
  • Strong Focus on Hands-on Training: Hands-on training holds significant pedagogical value as it reinforces theoretical concepts and lowers the entry barrier for novice users, who benefit from direct guidance and interaction with expert developers and users of CMS, AI/ML, and AHPC tools.
  • Hybrid Program Flexibility: CMS3-FAST offers participants the flexibility to attend either online or in-person, allowing them to choose the mode of learning that best suits their needs. Additionally, both the lecture and hands-on training components seamlessly transition between online and in-person formats, ensuring that participants can access the program’s valuable content regardless of their location or preferred learning style.

Who Should Attend CMS3-FAST:

  • Graduate Students aiming to learn more about the interdisciplinary fields of CMS, AI/ML techniques, along with AHPC, and their integration.
  • Academics, Scientists, and Engineers seeking to enhance their expertise in these fields to accelerate advancements in Materials Science and Engineering or stay at the forefront of technology and innovation.

What Does It Cost: The school is free for all participants. Additionally, a limited number of fellowships are available to qualified in-person participants. These fellowships will cover accommodation during the school and provide partial travel support for graduate students enrolled in US universities. For graduate students enrolled in international universities, the fellowships will cover accommodation during the school. A Note for Texas A&M University students: Those eligible to enroll in MSEN 657 Multiscale Modeling in Materials for credit will need to pay associated university tuition and fees and must consult with their Thesis/Dissertation Advisor regarding funds to cover these expenses.