Energy

１0．特許※ 上記ご研究に関連する主要特許があれば番号のご記載をお願いします。

Green innovation, Energy production, Materials informatics（記載例）特願 2021-○○○○○○，特開 2021-○○○○○○，特許第○○○○○○○○○○，US○○○○○○○など

Development of next-generation solar cell by combining
experiments and data science１2. ご自身の顔写真のご提供

１1．上記ご研究に関連する画像（図表・イラスト・写真）※ 印刷可能な解像度のものを電※ A4 版では 2～3 点（主要図表＋補足図表），A5 版では 1～2 点（主要図表の貼り付けを予定しておりますので，ご提供をお願い致します。

※ 必ず主要図表のご指定をお願い致します。

Department of Applied Chemistry, Graduate School of Engineering※ 画像は印刷可能な解像度のものを電子ファイルにてご提供ください。ペProfessor Akinori Saekiの関係で問題が生じた場合は，こちらでトリミング等の加工をさせていあります。

Researchmap https://researchmap.jp/a-saeki/

主要Abstract
High-efficiency, low-cost next-generation solar cells have beendeveloped as a sustainable energy source, and organic solar cells
that do not contain toxic elements are one of the most plausible
candidates. In this research, we perform a high throughput exploration of solar cell materials by combining a unique evaluation
method based on microwave spectroscopy and machine learning.
Organic-inorganic hybrid materials such as perovskite solar cells
are other materials of target.

Energy▍ Background & Results

英語版

Compared to commercially-available inorganic solar cells suchas silicon, organic solar cells are expected to be used on building
walls, owing to the light weight and low cost production. However, there are infinite chemical structures of organic materials, andtherefore it takes a lot of time and effort to design, synthesize, andevaluate new materials. In this research, we focus on polymer solar
cells consisting of polymers and non-fullerene electron acceptors,
which is designed by a machine learning model based on experimental data. A high throughput screening of polymer materials
revealed the top-ranked new polymers, which were actually synthesized and characterized. The experimental results of the solar cells
showed a good agreement with the machine learning prediction.
Accordingly, we have succeeded in demonstrating the effectiveness of material development, which opens up a new avenue to thedevelopment of more efficient polymer solar cells and the researchthat integrates experiments and machine learning in materials science.

▍ Significance of the research and Future perspectiveIn this research, we have developed an efficient experimental
screening method using a unique microwave spectroscopy anda machine learning model to explore polymer materials for organic solar cells at a very high speed. We constructed a machinelearning model using polymer and non-fullerene solar cells andscreened virtually generated 200,000 types of polymers. Based on
this result, a new polymer was synthesized and compared with thepredicted value of machine learning, and a good agreement wasobtained. In particular, the machine learning model was able topredict the structure of the optimum alkyl chain shape and length
for the specific polymer backbone. The high-precision machinelearning model demonstrated in this research can be a powerfultool for future polymer solar cell development.

P a t e n t

１2. ご自身の顔写真のご提供

Kranthiraja, K; Saeki, A. Experiment-Oriented Machine Learning of Polymer: Non-Fullerene Organic Solar Cells. Adv. Funct. Mater. 2021; 31: 2011168. doi:
※ 印刷可能な解像度のものを電子ファイルにてご提供ください。
10.1002/adfm.202011168
Saeki, A. Evaluation-oriented exploration of photo energy conversion systems: from fundamental optoelectronics and material screening to the combinaTr e a t i s etion with data science. Polym. J. 2020; 52: 1307-1321. doi: 10.1038/s41428-020-00399-2
Saeki, A; Yoshikawa, S; Tsuji, M. et al. A Versatile Approach to Organic Photovoltaics Evaluation Using White Light Pulse and Microwave Conductivity. J.
Am. Chem. Soc. 2012; 134:19035-19042.
http://www.chem.eng.osaka-u.ac.jp/~saeki/cmpc/
URL
https://publons.com/researcher/1310687/akinori-saeki/
K e y w o r d solar cell, machine learning, microwave spectroscopy, organic semiconductor, perovskite