Across the physical world, many intricate structures form via symmetry breaking. When a system with inherent symmetry ...
Researchers at Chungnam National University have developed a deep learning method that predicts stable defect configurations in nematic liquid crystals in milliseconds rather than hours. This rapid ...
Scientists have developed an AI system that can rapidly predict complex defect patterns in liquid crystals, cutting simulation times from hours to milliseconds. The approach could transform how ...
Order doesn’t always form perfectly—and those imperfections can be surprisingly powerful. In materials like liquid crystals, ...
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Alloy design model offers faster, more accurate predictions by factoring in material defects
The new model takes into account an important class of material defects (grain boundaries) and the tendency of the mixed solutes to gather—or segregate—around the structural imperfections during alloy ...
When we talk about defects, we generally think of flaws or impairments. However, as far as materials science is concerned, defects represent windows of opportunity. A new Collaborative Research Center ...
A recent review article published in Advanced Materials explored the potential of artificial intelligence (AI) and machine learning (ML) in transforming thermoelectric (TE) materials design. The ...
Two-dimensional (2D) materials show great promise for photocatalysis, a key technology for sustainable energy solutions like water splitting. However, optimizing their performance requires precise ...
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