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Reinforcement Learning Approach to High-Efficiency Thermophotovoltaic Filter Design.

Paulina V Escobar1,2, Hang Wang1, Junshan Zhang1

  • 1Department of Electrical and Computer Engineering, University of California, Davis, California 95616, United States.

ACS Applied Materials & Interfaces
|December 29, 2025
PubMed
Summary
This summary is machine-generated.

Deep reinforcement learning designs advanced optical filters for thermophotovoltaic (TPV) systems. This approach optimizes spectral matching, predicting over 50% efficiency for TPV energy conversion.

Keywords:
deep reinforcement learningmachine learningoptical filtersthermophotovoltaicstransfer matrix method

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Area of Science:

  • Energy Conversion
  • Materials Science
  • Optical Engineering

Background:

  • Thermophotovoltaic (TPV) systems efficiently convert thermal radiation to electricity, especially from waste heat.
  • Optimizing TPV efficiency requires precise spectral matching between the thermal source and photovoltaic cell.
  • Designing multilayer optical filters for spectral control presents complex optimization challenges.

Purpose of the Study:

  • To develop a deep reinforcement learning (DRL) framework for designing high-performance multilayer optical filters for TPV systems.
  • To enable selective transmission of photons with energies above the photovoltaic bandgap.
  • To maximize TPV system power conversion efficiency by reflecting unwanted photons.

Main Methods:

  • Integration of transfer matrix method simulations with a DRL framework.
  • Utilization of a customized reward function to guide filter design.
  • Incorporation of the detailed balance model to predict system performance.

Main Results:

  • Demonstration of DRL-designed filters approximating ideal spectral profiles.
  • Prediction of TPV efficiencies exceeding 50% for silicon PV cells.
  • Achieved high efficiencies at emitter temperatures below 1500 °C.

Conclusions:

  • DRL offers a scalable, data-driven approach for designing advanced optical components in TPV systems.
  • The developed framework effectively addresses the spectral matching challenge in TPV design.
  • This method paves the way for next-generation, high-efficiency energy conversion systems.