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Machine learning method improves semiconductor band gap predictionsThat's the kind of technology that researchers at Kyoto University have developed for the band gap of semiconductor materials. The work is published in the journal Computational Materials Science.
Techniques for Band Gap Engineering vary, including doping, the introduction of impurities into a semiconductor to alter its electrical properties; strain engineering, where stress is applied to ...
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