Development of Materials Informatics for High Efficiency Organic Thin-Film Solar Cells
Abstract
Organic thin-film solar cells are environmentally friendly, lightweight, and flexible solar cells that can be mass-produced at low cost by using roll-to-roll and other low-temperature printing processes on plastic substrates, and are expected to find a wide range of applications as ubiquitous power supply devices. In the field of organic thin-film solar cells, thin-film solar cells using non-fullerene acceptors have been attracting attention in recent years because of the strong absorption in the visible region, in which excitons can be generated efficiently even in the acceptor layer. Recently, thin-film solar cells with high photoelectric conversion properties exceeding 19% have been reported. Organic thin-film solar cells are extremely interesting not only for their application as solar cells but also as targets of research in condensed matter physics, since their photoelectric conversion function is realized through a complex interplay of various physical phenomena such as light absorption, excitation diffusion, charge separation, and carrier transport, and many experimental and theoretical studies have been conducted. Against this background, this proposal addresses the development of high-efficiency organic thin-film solar cell materials by utilizing an exploratory artificial intelligence system. The development of a search-based artificial intelligence system is already underway, led by Handa. We have developed a molecular search system based on evolutionary machine learning that combines Monte Carlo tree search (MCTS) and a deep learning model based on quantum chemistry (QDF), and it is currently capable of searching for molecules with large oscillator strength from scratch. In this project, we will further develop and perfect this search-based artificial intelligence system for the design of actual high-efficiency thin-film solar cell materials, and demonstrate its usefulness by actually synthesizing the organic thin-film solar cell materials proposed by the search-based artificial intelligence system and applying them to organic thin-film solar cells
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 05, 2025
- Source ID
- FA23862414008
Entities
People
- Takashi Okubo
Organizations
- Air Force Office of Scientific Research
- Kindai University
- United States Air Force