2025-08-20 北海道大学
図 1．⽂献データから材料合成までの道筋
＜関連情報＞
- https://www.hokudai.ac.jp/news/2025/08/aiai.html
- https://www.hokudai.ac.jp/news/pdf/250820_pr.pdf
- https://pubs.rsc.org/en/content/articlelanding/2025/sc/d5sc04813c
高度に練り上げた記述子によるバンドギャップを制御したペロブスカイト合成 Designing and synthesizing perovskites with targeted bandgaps via tailored descriptors
Kenshin Shibata, Fernando Garcia-Escobar, Tomoya Tashiro, Lauren Takahashi and Keisuke Takahashi
Chemical Science Published:19 Aug 2025
DOI:https://doi.org/10.1039/D5SC04813C
Abstract
Descriptors that govern the bandgaps of perovskite-type oxides are identified by analyzing experimentally reported materials, focusing on compositional, structural, and electronic features relevant to solar energy conversion. These descriptors form the basis of a machine learning model that predicts bandgaps across a wide chemical space. Several compositions with targeted optical properties are predicted and subsequently synthesized. Structural and optical characterization studies confirm the formation of the predicted phases and the bandgap. Thus, this work demonstrates that the descriptor-driven, data-guided workflow accelerates the discovery of photoactive perovskites for solar energy conversion and visible-light-driven applications.
