Understanding defect dynamics and evolution in high entropy alloys (HEAs) s is complicated due to the wide and intricate configurational space in HEAs. Machine learning techniques have significant ...

Using a new physics-informed machine learning approach, researchers discovered two new high-entropy alloys with extremely low thermal expansion, a new study reports. The approach could represent a ...

Since their discovery at Drexel University in 2011, MXenes — a family of nanomaterials with unique properties of durability, ...

Keeping reduction of computation time as the objective, here we present, an entropy query by bagging (EQB)-based AL approach in the extreme learning machine (ELM) framework for remote sensing image ...

In an article recently published in the open-access journal npj Computational Materials, researchers discussed the intelligent framework based on machine learning (ML) for finding refractory ...

In addition, the instrument will use machine learning to determine whether a change in measurements could be a temporary outlier, like a bird swimming by, and whether or not a surface is liquid water.

Entropy is a confusing concept, but it is also extremely useful for quantifying the properties of complex systems like the human brain. This article attempts to demystify the term.

Using data from historic ship measurements and Argo floats, researchers introduced a machine learning technique that improves assessment and analysis of the ocean's declining oxygen levels.

Entropy can measure a brain's computational complexity by quantifying the number of accessible mental states (the size of the mental repertoire).