A team of scientists at PNNL developed new computational models to predict the behavior of these impurities and reduce the expense and risk related to actinide metal production.
Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.
IDREAM research shows that keeping only the most important two- and three-body terms in reactive force fields can decrease computational cost by one order of magnitude, while preserving satisfactory accuracy.
Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.
PNNL researchers design liquid-based porous electrolyte that could transport lithium ions more easily between electrodes, improving battery efficiency.
A team of researchers developed a simulation approach to identify how atomic structures can affect the phonon transport of energy and information in quantum systems near absolute zero temperatures.
Theoretical work shows that an important natural iron source can be described as a nanoscale composite of different, but experimentally indistinguishable, structures.