Students and researchers nationwide are accelerating their materials science research thanks to Texas A&M University (TAMU) Computational Materials Science Summer School: Fostering Accelerated Scientific Techniques (CMS³-FAST). Funded by the U.S. National Science Foundation (NSF) CyberTraining program and supported by NSF’s ACCESS allocations ecosystem, the initiative integrates advanced computing, machine learning and visualization into one transformative training experience.
Directed by Lisa Perez, director for Advanced Computing Enablement in TAMU’s High Performance Research Computing Division, CMS³-FAST has been around for several years. The program unites computational materials science (CMS), artificial intelligence and machine learning (AI/ML), and high-performance computing (HPC) to prepare scientists for modern data-driven research.
“NSF ACCESS allocations on the ACES system here at TAMU are critical for democratizing computational research,” Perez explained. “Through CMS³-FAST, we’re not only teaching students how to conduct simulations and train models on supercomputers, we are also showing them how to navigate the national cyberinfrastructure that makes large-scale science possible.”
NSF ACCESS allocations on the ACES system here at TAMU are critical for democratizing computational research.
–Lisa Perez, director for Advanced Computing Enablement in TAMU’s High Performance Research Computing Division
The CMS³-FAST curriculum expands beyond computation. Participants use virtual and augmented reality (VR/AR) to visualize atomic-scale materials interactions, providing an intuitive way to interpret large datasets and complex molecular structures. This integration of immersive visualization with HPC and AI represents an emerging paradigm in science education and discovery.
Perez said that a core goal of the program is workforce development: addressing the growing need for scientists who can bridge materials science, data analytics and supercomputing.
“By leveraging NSF ACCESS allocations, CMS³-FAST ensures trainees gain both practical experience and an understanding of how to scale research workflows on national HPC platforms,” she said.
Supported by NSF Award #2321005, CMS³-FAST is part of a larger national effort to build cyberinfrastructure-savvy research communities equipped for future technological challenges.
Resource Provider Institution(s): Texas A&M (TAMU)
Resources Used: ACES
Affiliations: Texas A&M
Funding Agency: NSF
Grant or Allocation Number(s): TRA220029
The science story featured here was enabled by the U.S. National Science Foundation’s ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.