Texas Advanced Computing Center (TACC)

How do you design materials that can survive hypersonic flight?

At speeds above Mach 5, extreme heat and chemically aggressive plasma push materials to their limits. Researchers are turning to HPC and AI to accelerate the search for ultra-high-temperature ceramics.

Using National Science Foundation (NSF) ACCESS resource Stampede3 at TACC, a team led by Peter Kroll at The University of Texas at Arlington developed a machine learning model that enables simulations previously impossible with traditional quantum methods alone—advancing the design of next-generation aerospace materials.

“Supercomputing resources like Stampede3 are essential for this kind of research and make it happen. Harvesting data at this level of complexity—capturing chemical reactions and phase transitions across millions of atoms in multi-component systems—is what enables us to address future challenges, and it demands computational power far beyond what any single lab can provide.” -- Peter Kroll, UT Arlington

Learn more: https://access-ci.org/from-polymers-to-plasma-shields/

A new era of U.S. scientific computing is underway. 🚀

As the centerpiece supercomputer of the U.S. National Science Foundation (NSF) Leadership-Class Computing Facility (NSF LCCF), Horizon will empower researchers to tackle some of the nation's most ambitious scientific challenges.

Last month, more than 200 members of the open science community came together to explore how leadership-class computing—and the National Science Foundation (NSF) LCCF Characteristic Science Applications (CSA) program—is accelerating discoveries across disciplines.

From astronomy and earthquake science to quantum materials, lattice quantum chromodynamics, and biophysics, researchers shared how TACC's Frontera and Vista supercomputers are enabling breakthroughs that would be impossible without advanced computing.

Learn more about the CSA program and the pioneering research that will help define the future of science on Horizon: https://bit.ly/4vJncVU

Watch the virtual panel: https://bit.ly/4fl43Uq

The University of Texas at Austin

How do you move scientific AI models from development to real-world deployment?

New open-access research explores an end-to-end Machine Learning Operations (MLOps) pipeline that streamlines scientific AI workflows — from model development and automated deployment to continuous integration and deployment (CI/CD).

Congratulations to Manikya Swathi Vallabhajosyula, Nathan Freeman, Smruti Padhy, Christian R. Garcia, Gautam Gururaj Molakalmuru, Joe Stubbs, and Anagha Jamthe on this publication. 🎉

Learn more: https://bit.ly/4eGKbLd