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Machine learning method improves semiconductor band gap predictions


Published on 2025-02-10 15:42:19 - MSN
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  • 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 ]