AZOM

An Overview of Machine Learning Applied to High Entropy Alloys

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 ...
EurekAlert!

New machine learning approach for high-entropy alloy discovery

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 ...
EurekAlert!

Refractory high-entropy alloys reimagined: from phase design to extreme-environment performance

A new critical review published in Materials Futures traces the rapid evolution of Refractory High-Entropy Alloys (RHEAs), a revolutionary class of materials engineered for extreme environments. The ...
AZOM

Machine Learning Framework Drives Search for Better Refractory High-Entropy Alloys

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 ...
Science Daily

Deep learning for new alloys

Supercomputer simulations are helping scientists discover new high-entropy alloys. XSEDE allocations on TACC's Stampede2 supercomputer supported density function theory calculations for largest ...
JSTOR Daily

Entropy query by bagging-based active learning approach in the extreme learning machine framework for hyperspectral image classification

This is a preview. Log in through your library . Abstract Active learning (AL) technique is the classification of remote sensing images, where collecting efficient training data is costly in terms of ...