2025 年 16 巻 1 号 p. 12-20
In this paper, we propose a method for designing organic molecules with high light absorption, suitable for organic thin-film solar cells. By fragmenting existing compounds into sub-compounds and reassembling them, we generate candidate structures in an evolutionary algorithm framework. To evaluate these candidates efficiently, we use Quantum Deep Field (QDF), a deep learning method based on density functional theory, instead of conventional simulations with Gaussian16. This method makes it possible to perform a large-scale evolutionary search within a realistic time frame.
Through multiple experiments with various population sizes and offspring parameters, we identified an opti-mal parameter configuration for our evolutionary algorithm. We then confirmed that the detailed quantum chemical simulations (Gaussian16) show that the best-performing compounds discovered with our method have spectra with higher light absorption than several conventional organic solar cell materials. Furthermore, our analysis shows that expanding the search space and incorporating additional properties (e.g., absorption wavelength) could further en-hance the quality of discovered compounds. These findings highlight the potential of combining deep learning-based quantum approximations with evolutionary computation, opening new avenues for efficient and data-driven design of advanced photovoltaic materials.
