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Computation CHANGE TOPIC

Computation and theory news, January 2021

Squeezing a promising perovskite material in a diamond anvil cell produces a so-called 'black' phase that's stable enough for solar power applications.

A new model has revealed that the strength of carbon nanotube fibers depends on the length of the nanotubes and the friction between them.

Researchers have observed metallic line defects in a perovskite crystal, which offer a novel way to create materials that are transparent and conductive.

By enhancing an algorithm based on the nesting habits of cuckoo birds, scientists have greatly reduced the search time for new high-tech alloys.

By combining low-fidelity and high-fidelity data, researchers have developed a new machine-learning method to predict the properties of materials.

Researchers have developed a new method for predicting the specific colors of thin films made by combining any of 466 different carbon nanotubes.

Researchers have discovered that the surface atomic layer of a catalyst for splitting water can re-arrange itself to boost its catalytic activity.

Using machine learning, researchers have been able to complete cumbersome materials science calculations more than 40,000 times faster than normal.

Creating a uniform membrane density is crucial for maximizing the performance of polymer membranes for water desalination.

An inexpensive catalyst made from tiny clusters of nickel metal anchored to a 2D substrate is highly effective at extracting hydrogen from alcohols.

A novel machine-learning model can accurately predict the hardness of new materials based solely on their chemical composition.

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