RT Journal Article
T1 A novel machine learning workflow to optimize cooling devices grounded in solid-state physics
A1 García Fernández, Julián
A1 Etesse, Guéric
A1 Seoane Iglesias, Natalia
A1 Comesaña Figueroa, Enrique
A1 Hirakawa, Kazuhiko
A1 García Loureiro, Antonio Jesús
A1 Bescond, Marc
K1 Machine learning
K1 Cooling devices
K1 Solid-state physics
K1 Optimization
AB 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 (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.
PB Nature
SN 2045-2322
YR 2024
FD 2024-11-18
LK https://hdl.handle.net/10347/41933
UL https://hdl.handle.net/10347/41933
LA eng
NO Fernandez, 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/s41598-024-80212-9
NO This work was supported by the Spanish MICINN/AEI, Xunta de Galicia, and FEDER Funds under Grant RYC-2017-23312, Grant PID2019-104834GB-I00, Grant PID2022-141623NB-I00, Grant PID2022-142709OB-C21/PID2022-142709OA-C22, Grant ED431F 2020/008, Grant ED431C 2022/16 and GELATO ANR project (ANR-21-CE50-0017).
DS Minerva
RD 25 abr 2026