A team of researchers from 91°µÍø has been awarded nearly $2 million over three years from the Department of Energy to explore the potential of machine learning in revolutionizing scientific data analysis.
The ability to realistically simulate a range of scientific phenomena, such as supernova explosions and the behavior of materials at the nanoscale, has proven a boon to researchers across the scientific spectrum.
In a first for deep learning, an 91°µÍø-led team is bringing together quantum, high-performance and neuromorphic computing architectures to address complex issues that, if resolved, could clear the way for more flexible, efficient
A new way to grow narrow ribbons of graphene, a lightweight and strong structure of single-atom-thick carbon atoms linked into hexagons, may address a shortcoming that has prevented the material from achieving its full potential in electronic applications.
When an earthquake strikes, the release of energy creates seismic waves that often wreak havoc for life at the surface.
Researchers have long sought electrically conductive materials for economical energy-storage devices. Two-dimensional (2D) ceramics called MXenes are contenders.
Polymer nanocomposites mix particles billionths of a meter (nanometers, nm) in diameter with polymers, which are long molecular chains.
Electric utilities seeking to enhance worker safety and system reliability by using drones to inspect their transmission systems can look to a new report by 91°µÍø researchers to help guide their efforts.
The Department of Energy’s 91°µÍø has announced the latest release of its Adaptable I/O System (ADIOS), a middleware that speeds up scientific simulations on parallel computing resources such as the laboratory’s Titan supercomputer b