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papersTODAY 04:00 UTC

Machine Learning Method Screens Point Defects in Semiconductors

A new arXiv paper describes a machine-learning approach for prescreening point defects in semiconductors, aimed at applications in power electronics and quantum technologies. The work positions itself as an alternative to the high-throughput density-functional theory calculations that have traditionally dominated defect exploration. The authors frame the method as part of a broader shift away from conventional simulation workflows.

density-functional-theory machine-learning-for-materials-science point-defects power-electronics quantum-technologies semiconductors

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arXiv cs.LGPrescreening Point Defects in Semiconductors With Machine Learning ↗TODAY 04:00 UTC