A novel machine learning workflow to optimize cooling devices grounded in solid-state physics - Archive ouverte HAL
Article Dans Une Revue Scientific Reports Année : 2024

A novel machine learning workflow to optimize cooling devices grounded in solid-state physics

Résumé

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 (T e ). Using a vast search space of 1.18 × 10 -5 different device configurations, we obtained a set of optimum devices with prediction relative errors lower than 4 % for CP and 1 % for T e . 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.

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Dates et versions

hal-04740369 , version 1 (16-10-2024)

Identifiants

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Julian G Fernandez, Guéric Etesse, Natalia Seoane, Enrique Comesaña, Kazuhiko Hirakawa, et al.. A novel machine learning workflow to optimize cooling devices grounded in solid-state physics. Scientific Reports, 2024, 14 (1), pp.28545. ⟨10.1038/s41598-024-80212-9⟩. ⟨hal-04740369⟩
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