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Machine learning method improves semiconductor band gap predictions

  Copy link into your clipboard //science-technology.news-articles.net/content/2 .. improves-semiconductor-band-gap-predictions.html
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          🞛 This publication is a summary or evaluation of another publication 🞛 This publication contains editorial commentary or bias from the source
Imagine you're cooking. You're trying to develop a unique flavor by mixing spices you've never combined before. Predicting how this will turn out could be tricky. You want to create something delicious,
The article from MSN discusses a new machine learning method developed by researchers at the University of California, Los Angeles (UCLA), aimed at improving the prediction of semiconductor band gaps. Band gaps are crucial for determining the electronic properties of materials, which in turn affects their application in electronics and optoelectronics. Traditional methods for predicting band gaps, like density functional theory (DFT), often fall short in accuracy due to their computational complexity and approximations. The new approach leverages machine learning to enhance the accuracy of these predictions by training on a vast dataset of known materials. This method not only speeds up the prediction process but also provides more precise results, potentially accelerating the discovery and design of new materials for advanced technologies, including more efficient solar cells, LEDs, and transistors.

Read the Full MSN Article at:
[ https://www.msn.com/en-us/technology/hardware-and-devices/machine-learning-method-improves-semiconductor-band-gap-predictions/ar-AA1yLYmh ]