Prenzler Group initiates coverage of the clinical-stage, psilocin-focused Entropy Neurodynamics. ... Read More The post New ...

High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations.

High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations.

Abstract: Machine learning is widely used to solve networking challenges, ranging from traffic classification and anomaly detection to network configuration. However, machine learning also requires ...

In order to build the computers and devices of tomorrow, we have to understand how they use energy today. That's harder than it sounds. Memory storage, information processing, and energy use in these ...

Many high-performance HEAs used in areas such as aerospace engines, gas turbines, and nuclear power plants employ CRMs such as tantalum, niobium, tungsten, and hafnium. These elements are expensive, ...

Guwahati, in collaboration with colleagues from the London South Bank University, the University of Manchester, and the University of Leeds, have developed a machine learning (ML)-based method to ...

Abstract: Nonstabilizerness is a fundamental resource for quantum advantage, as it quantifies the extent to which a quantum state diverges from those states that can be efficiently simulated on a ...