Deep Learning Uses Stream Discharge to Estimate Watershed Subsurface Permeability
Researchers used deep learning methods to estimate the subsurface permeability of a watershed from readily available stream discharge measurements.
Researchers used deep learning methods to estimate the subsurface permeability of a watershed from readily available stream discharge measurements.
Combining synthesis, characterization, and theory confirmed the exotic properties and structure of a new intrinsic ferromagnetic topological material.
Neutrons reveal remarkable atomic behavior in thermoelectric materials for more efficient conversion of heat into electricity.
The results may offer insight into the quark-gluon plasma—the hot mix of fundamental nuclear-matter building blocks that filled the early universe.
Studies of the nanostructure of a chiral magnet provides insights on controlling magnetic properties for applications in computers and other electronics.
New optics-on-a-chip device paves the way to helping characterize fast chemical, material, and biological processes.
Neutron scattering monitors structures during post-production heat treatment to validate production models.
Short and long-range electron transfer compete to determine free-charge yield in organic semiconductors.
Neural networks determine the amplitude and phase of X-ray pulses, enabling new, high-resolution quantum studies.
Using two methods is better than one when it comes to observing how solar cells form and improving cell properties.
Novel molecular beam scattering apparatus that uses a liquid flat jet can study chemical reactions at the gas liquid interface of volatile liquids.
Discovery of a short-lived state could lead to faster and more energy-efficient computing devices.