A new review in the Journal of Materials Science maps how machine learning, from graph neural networks to large language models, is accelerating the design of high-entropy alloy catalysts across vast ...

A first-of-its-kind systematic review of 190 studies finds that HASM, Euclidean-enhanced machine learning, and Bayesian ...

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 ...

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 ...

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 ...

Entropy, the amount of molecular disorder, is produced in several systems but cannot be measured directly. An equation developed by researchers at Chalmers University of Technology in Sweden, and ...