Reinforcement Learning Approach to High-Efficiency Thermophotovoltaic Filter Design

Thermophotovoltaic (TPV) systems offer a promising route to convert thermal radiation into electricity with high efficiency, particularly when waste heat or high-temperature sources are available. A key challenge in TPV system design is to match the optical spectrum of the source to the absorption spectrum of the photovoltaic (PV) cell. While spectral control can be achieved through engineered emitters or optical filters, the design of high-performance, multilayer filters is a complex optimization problem due to the large parameter space and fabrication constraints. Here, we introduce a deep reinforcement learning (DRL) framework to design multilayer optical filters that selectively transmit a narrowband of above-bandgap photons, while reflecting sub-bandgap and excessively high-energy photons back to the emitter. Our DRL-based approach integrates transfer matrix method simulations with a customized reward function and the detailed balance model to maximize the system's power conversion efficiency. We demonstrate filters that closely approximate ideal spectral profiles and predict TPV efficiencies exceeding 50% for silicon PV cells and emitter temperatures below 1500 °C. This method provides a scalable, data-driven pathway for designing advanced optical components in next-generation energy conversion systems.