| Autor: |
Julian G. Fernandez, Guéric Etesse, Natalia Seoane, Enrique Comesaña, Kazuhiko Hirakawa, Antonio Garcia-Loureiro, Marc Bescond |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-024-80212-9 |
| Popis: |
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 energetic and thermal properties. We propose a novel machine learning (ML) workflow to accelerate the design optimization process of these cooling devices, alleviating the high computational demands of NEGF+H. This methodology, trained with NEGF+H data, obtains the optimum heterostructure designs that provide the best trade-off between the cooling power of the lattice (CP) and the electron temperature ( $$ {\text{T}}_{e} $$ ). Using a vast search space of $$1.18 \times 10^{-5}$$ different device configurations, we obtained a set of optimum devices with prediction relative errors lower than $${4}\,\%$$ for CP and $${1}\,\%$$ for Te. The ML workflow reduces the computational resources needed, from two days for a single NEGF+H simulation to 10 s to find the optimum designs. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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