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Computation CHANGE TOPIC

Computation and theory news, September 2020

Scientists have used artificial intelligence to identify a novel class of cheap, stable solid electrolyte materials made from lithium, boron and sulfur.

Researchers have found that aluminum nanoparticles with sharper corners are better able to utilize light to catalyze chemical reactions.

A novel framework for identifying and assessing the spin textures of crystalline materials could help develop low-energy computing technology.

A novel model for predicting and improving battery performance is 100,000 times faster than existing modeling techniques.

A machine learning approach to predicting the quality of 3D-printed bioscaffolds has revealed the importance of controlling print speed.

Researchers have demonstrated on computer a novel color-coding system for getting DNA to self-assemble into desired nanostructures.

Researchers have uncovered the atomic mechanism that makes tin-based thermoelectric materials incredibly efficient at high temperatures.

Adding elliptical holes to elastic materials can enhance their efficiency at converting elastic energy to kinetic energy for extreme velocity movements.

A novel artificial neural network can predict the degree of water repulsion and protein adsorption by ultra-thin organic materials.

For the first time, researchers have developed a way to measure the individual solid-like and liquid-like behaviors of soft materials separately.

By combining computational modeling with experimental data, researchers have developed a novel approach for tracking ions within a 2D material.

A novel mathematical procedure can reduce the computational cost of modelling quantum materials via the quantum Monte Carlo method.

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