Feature-Assisted Machine Learning for Predicting Band Gaps of Binary Semiconductors
The band gap is a key parameter in semiconductor materials that is essential for advancing optoelectronic device development. Accurately predicting band gaps of materials at low cost is a significant challenge in materials science. Although many machine learning (ML) models for band gap prediction a...
I tiakina i:
| Ngā kaituhi matua: | , , , |
|---|---|
| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
MDPI AG
2024-02-01
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| Rangatū: | Nanomaterials |
| Ngā marau: | |
| Urunga tuihono: | https://www.mdpi.com/2079-4991/14/5/445 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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