Updated software improves slicing for large-format 3D printing
ORNL’s Debangshu Mukherjee has been named an npj Computational Materials “Reviewer of the Year.”
The Oak Ridge Leadership Computing Facility’s Matt Sieger has been named the project director for the OLCF-6 effort. This next OLCF undertaking will plan and build a world-class successor to the OLCF’s still-new exascale system, Frontier.
Led by Kelly Chipps of ORNL, scientists working in the lab have produced a signature nuclear reaction that occurs on the surface of a neutron star gobbling mass from a companion star. Their achievement improves understanding of stellar processes generating diverse nuclear isotopes.
With the world’s first exascale supercomputing system now open to full user operations, research teams are harnessing Frontier’s power and speed to tackle some of the most challenging problems in modern science.
Kelly Chipps, a nuclear astrophysicist at ORNL, has been appointed to the Nuclear Science Advisory Committee, or NSAC. The committee provides official advice to DOE and the National Science Foundation, or NSF, about issues relating to the national program for basic nuclear science research.
Anne Campbell, an R&D associate at ORNL, has been selected for an Emerging Professional award from ASTM International. ASTM, formerly the American Society for Testing and Materials, is an international standards organization that develops and publishes voluntary consensus technical standards for a wide range of materials, products, systems and services.
Valentino “Tino” Cooper, a scientist at ORNL, has been appointed to DOE’s Basic Energy Sciences Advisory Committee for a three-year term. Cooper’s research elucidates the fundamental understanding of advanced materials for next-generation energy and information technologies.
Andrew Lupini, a scientist and inventor at ORNL, has been elected Fellow of the Microscopy Society of America.
Marm Dixit, a Weinberg Distinguished Staff Fellow at ORNL has received the 2023 Rosalind Franklin Young Investigator Award.
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.