Machine learning-optimized Tamm emitter for high-performance thermophotovoltaic system with detailed balance analysis

Light-matter interaction upon nanophotonic structures in the infrared wavelength has drew increasing attentions due to the extensive potential applications. Among them, thermophotovoltaic (TPV) systems can exhibit higher efficiency over the Shockley-Queisser limit due to the nanophotonic structure-enabled tunable narrowband thermal emission rather than the broadband incident spectrum. However, two long-standing issues remain formidable as bottlenecks for achieving better performances of TPV system. One is the competing role of the power density and the system efficiency of TPV system, and the other is the magnanimity possibilities of structures, configurations, dimensions, and materials of thermal emitters that disables the manual optimization of TPV system. Here, we attempt to achieve high-performance TPV system by employing the machine learning algorithm under the framework of material informatics. The power density and system efficiency are well modelled through the detailed balance analysis with full considering the photocurrent generation in the PV cells. Through optimization, the non-trial aperiodic Tamm emitters are obtained and the metal-side one is preferable in terms of the TPV performance. The present work is demonstrated to be feasible and efficient in optimizing the TPV performance, and opens a new door for the optimization problems in other fields.