A novel machine learning workflow to optimize cooling devices grounded in solid-state physics
Abstract Cooling devices grounded in solid-state physics are promising candidates for integrated-chip nanocooling applications. These devices are modeled by coupling the quantum non-equilibirum Green’s function for electrons with the heat equation (NEGF+H), which allows to accurately describe the en...
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| Main Authors: | Julian G. Fernandez, Guéric Etesse, Natalia Seoane, Enrique Comesaña, Kazuhiko Hirakawa, Antonio Garcia-Loureiro, Marc Bescond |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-80212-9 |
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