High-entropy alloys (HEAs) are rewriting the rules of materials science, and machine learning is accelerating their design. By predicting phase stability and performance from large datasets, ...

From atomic-scale imaging to machine learning predictions, AI is revolutionizing how scientists design and optimize high-entropy alloys. These materials, prized for their strength and resilience, are ...

Composed of five or more elements in nearly equal amounts, high-entropy alloys (HEAs) have emerged as promising catalysts due ...

The rapid increase in electric vehicle adoption in recent years has highlighted a crucial issue: the energy conversion ...

Composed of five or more elements in nearly equal amounts, high-entropy alloys (HEAs) have emerged as promising catalysts due ...

Magnetic domains can take on a wide range of structures. In certain soft magnetic materials, they form complex zig-zag ...

Maze magnetic domains in soft magnetic materials strongly influence energy loss in electric motors, particularly at high ...

Researchers develop a new computational model that helps identify origin of complex magnetization reversal in soft magnets.

The rapid increase in electric vehicle adoption in recent years has highlighted a crucial issue: the energy conversion efficiency of electric motors ...

Researchers also discovered that liquid gallium is not entirely disordered at its surface, forming subtle, layered atomic ...

Prompt engineering keeps adding new techniques. One is the String Seed-of-Thought (SSoT) that aids options-choosing, game ...