García Fernández, JuliánEtesse, GuéricSeoane Iglesias, NataliaComesaña Figueroa, EnriqueHirakawa, KazuhikoGarcía Loureiro, Antonio JesúsBescond, Marc2025-06-022025-06-022024-11-18Fernandez, J. G., Etesse, G., Seoane, N., Comesana, E., Hirakawa, K., Garcia-Loureiro, A., & Bescond, M. (2024). A novel machine learning workflow to optimize cooling devices grounded in solid-state physics. “Scientific Reports”., vol. 14, 28545 https://doi.org/10.1038/s41598-024-80212-92045-2322https://hdl.handle.net/10347/41933Cooling 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 (Te). 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 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.eng© The Author(s) 2024Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Machine learningCooling devicesSolid-state physicsOptimization33 Ciencias tecnológicasjournal article10.1038/s41598-024-80212-9open access