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Long predicted by theory with support from supercomputers, this combination of neutrons advances nuclear physics
Quantum technique accelerates identification of entangled materials.
Cloud microphysics affect precipitation extremes on multiple time scales in climate models.
A new UV-visible protein allows researchers to see gene expression in plants without special equipment.
Researchers used deep learning methods to estimate the subsurface permeability of a watershed from readily available stream discharge measurements.
The Facility for Rare Isotope Beams has demonstrated an innovative liquid-lithium charge stripper to accelerate unprecedentedly high-power heavy-ion beams.
Research finds that the effects of drought and wildfire on soil bacterial communities fade in deeper soils.
Combining synthesis, characterization, and theory confirmed the exotic properties and structure of a new intrinsic ferromagnetic topological material.
Using Earth-based particle accelerators, scientists measure the reactions that take place in stars to produce carbon.
Scientists develop a new learning method that incorporates quantum chemistry descriptions with conventional machine learning to predict the properties of biochemical molecules.
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.
