BIOENGINEER

Entropy-weighted feature selection lifts heart disease prediction to 94 percent accuracy

A new entropy-weighted feature selection method trims 13 clinical attributes down to 8 and pushes heart disease prediction ...
Nature

Machine Learning Techniques in High-Entropy Alloy Design articles from across Nature Portfolio

Refractory high-entropy alloys (RHEAs) are compositionally complex materials which have been demonstrated to have the potential for exceptional strength at high operating temperatures. However, their ...
Nature

Machine Learning Techniques in High-Entropy Alloy Design

High-entropy alloys (HEAs) represent a transformative class of structural materials defined by near-equimolar proportions of five or more elements. The vast compositional space and complex phase ...
Lab Manager

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 ...
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!

From trial-and-error to intelligent design: Machine Learning boosts a breakthrough in the performance of BaTiO3-based High-Entropy energy-storage ceramics

Dielectric ceramic capacitors are essential core components for electronics, smart grids and new energy vehicles, prized for their high power density. As electronic devices move toward miniaturization ...