Computation CHANGE TOPIC

Computation and theory news, November 2021

Researchers have reported the first measurements of the ultra-low-friction behavior of a 2D material known as magnetene.

Researchers turned an inert 2D material into a chemically active catalytic support by covering it in tiny holes filled with precious metal atoms.

Removing tiny irregularities in local densities can help prevent the atomic 'avalanches' that cause glassy materials to transform into crystal.

Researchers predict that growing 2D boron, known as borophene, on hexagonal boron nitride should make it easier to remove and study.

Using a scanning probe microscope, researchers have been able to determine the quantum interactions that give rise to a stable standing molecule.

A new analysis technique will help scientists create better supercapacitors, especially supercapacitors made from layers of different materials.

Researchers took advantage of the artificial intelligence technology behind deepfakes to create novel high-performance alloys.

Researchers have developed a method for calculating the wavefunctions of electrons in a material from physical measurements.

An exploration of the amazing world of surface science.

Researchers have probed the relationship between charge density waves and the strange metal state in a high-temperature superconductor.

By combining copper with cellulose nanofibrils from wood, researchers have developed a flexible ion conductor for solid-state batteries.

Researchers have discovered that interactions between the layers of nacre in a pearl cause its symmetry to become more and more precise as it grows.

Researchers have uncovered the first evidence that electrons in a superconductor can condense into foursomes that break time-reversal symmetry.

Researchers have discovered why applying pressure to a lithium-metal battery can boost its performance, and determined the optimum pressure.

Using atomic electron tomography, researchers have, for the first time, directly observed how atoms are packed in amorphous materials.

Researchers have developed a machine-learning system that can optimize new 3D printing materials with multiple mechanical properties.

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