The MarketWatch News Department was not involved in the creation of this content. Deep learning reduces simulation times from hours to milliseconds DAEJEON, South Korea, Jan. 27, 2026 /PRNewswire/ -- ...
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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 ...
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 ...
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 ...
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Ultra-thin electronics to become more efficient with US researchers’ technique to spot defects
Researchers in the United States have developed a new technique that can spot hidden ...
DAEJEON, South Korea, Jan. 27, 2026 /PRNewswire/ --Topological defects govern how many advanced materials behave, but predicting them has traditionally required slow, resource-intensive simulations.
DAEJEON, South Korea, Jan. 27, 2026 /PRNewswire/ --Topological defects govern how many advanced materials behave, but predicting them has traditionally required slow, resource-intensive simulations.
The AI model rapidly maps boundary conditions to molecular alignment and defect locations, replacing hours of simulation and enabling fast exploration and inverse design of advanced optical materials.
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