Computation CHANGE TOPIC

Computation and theory news, March 2019

Researchers have used machine learning to create molecular models for faster screening of materials such as organic electronics.

By exciting ‘frustrated’ crystalline materials with laser light, a team of researchers has managed to transform them into a stable ‘supercrystal’.

Researchers have discovered the strongest topological conductor yet, in the form of thin crystal samples with a spiral-staircase structure.

Scientists have identified the causes of degradation in a nickel-rich layered cathode material in lithium-ion batteries, as well as possible remedies.

Strength of metallic alloys that improves at cryogenic temperatures

Scientists have used a new technique called ultrafast surface X-ray scattering to visualize the motion of atoms in a 2D material.

Acoustic metamaterial structure blocks 94% of sound

Researchers have discovered that a high-strength polymer called PBDT has a rare double-helix structure.

Simulations of how surface roughness changes over time have revealed that surfaces are worn down by the action of debris particles between them.

By combining graphene with nano-ribbons of gold, researchers have developed an ultrasensitive biosensor for detecting disease-causing proteins.

The twist angle between layers of 2D semiconductors provides a ‘tuning knob’ to turn them into an exotic quantum material.

By combining supercomputing with experimental methods, researchers have been able to identify promising materials for dye-sensitized solar cells

Researchers used machine learning to simulate the quantum mechanics analysis of two capacitor materials, greatly speeding up the process.

Novel simulations show that atom-sized steps on a substrate have the remarkable ability to keep monolayer crystal islands in alignment as they grow.

The energy storage capacity of electrodes made from the 2D material MXene can be doubled by adding an appropriate solvent to the electrolyte.

Researchers have developed a blueprint for understanding and predicting the properties and behavior of nanoparticles containing multiple elements.

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