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

Abstract

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.