  In this work, Machine Learning (ML) techniques have been employed to explore
the highest performing single-heteronunction thermophotovoltaic cell.
Initially, traditional homo junction TPV cells have been explored using ML
methodologies for the optimal material combinations. ML methods have notably
been devoted to analyze the importance of each parameter in the model, thereby
improving the comprehension of the system's behavior and facilitating design
optimization. Following this investigation, it has been found that Ge emerged
as the most effective emitter layer when paired with the optimal base layer,
InGaAsSb compound that possesses a direct bandgap of 0.53 eV. Subsequently, a
p-Ge/n-InGaAsSb single-heterojunction TPV cell is introduced executing a device
transport model featuring a p-n structure. This cell operated at black body
(TBB) and cell temperatures of 1578 K and 300 K, respectively. Through
meticulous optimization efforts, the performance of the TPV cell is
significantly enhanced resulting in an impressive efficiency of 16.50%. This
efficiency is accompanied by a short circuit current, JSC=15.53 A/cm2, an
open-circuit voltage, VOC=0.47 V, and a fill factor FF=79.5%. These findings
suggest that this structural configuration holds considerable promise for the
development of high-performance TPV cells.
