Balaprakash chosen for Tennessee’s new AI advisory council
The contract will be awarded to develop the newest high-performance computing system at the Oak Ridge Leadership Computing Facility.
To better predict long-term flooding risk, scientists at the Department of Energy’s Oak Ridge National Laboratory developed a 3D modeling framework that captures the complex dynamics of water as it flows across the landscape. The framework seeks to provide valuable insights into which communities are most vulnerable as the climate changes, and was developed for a project that’s assessing climate risk and mitigation pathways for an urban area along the Southeast Texas coast.
A collection of seven technologies for lithium recovery developed by scientists from ORNL has been licensed to Element3, a Texas-based company focused on extracting lithium from wastewater produced by oil and gas production.
Corning uses neutron scattering to study the stability of different types of glass. Recently, researchers for the company have found that understanding the stability of the rings of atoms in glass materials can help predict the performance of glass products.
A team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%.
Caldera Holding, the owner and developer of Missouri’s Pea Ridge iron mine, has entered a nonexclusive research and development licensing agreement with ORNL to apply a membrane solvent extraction technique, or MSX, developed by ORNL researchers to mined ores.
Technologies developed by researchers at ORNL have received six 2023 R&D 100 Awards.
Scientist-inventors from ORNL will present seven new technologies during the Technology Innovation Showcase on Friday, July 14, from 8 a.m.–4 p.m. at the Joint Institute for Computational Sciences on ORNL’s campus.
Andrew Lupini, a scientist and inventor at ORNL, has been elected Fellow of the Microscopy Society of America.
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.