工作流程
钙钛矿(结构)
反向
材料科学
计算机科学
纳米技术
工程类
化学工程
数据库
数学
几何学
作者
Jianchang Wu,Luca Torresi,Manli Hu,Patrick Reiser,Jiyun Zhang,Juan S. Rocha‐Ortiz,Luyao Wang,Zhiqiang Xie,Kaicheng Zhang,Byung‐wook Park,Anastasia Barabash,Yicheng Zhao,Junsheng Luo,Yunuo Wang,Larry Lüer,Lin‐Long Deng,Jens Hauch,Dirk M. Guldi,M. Eugenia Pérez‐Ojeda,Sang Il Seok,Pascal Friederich,Christoph J. Brabec
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2024-12-12
卷期号:386 (6727): 1256-1264
标识
DOI:10.1126/science.ads0901
摘要
The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential but has not yet been realized. Current models rely on large data sets that generally do not exist for specialized research fields. We demonstrate a closed-loop workflow that combines high-throughput synthesis of organic semiconductors to create large datasets and Bayesian optimization to discover new hole-transporting materials with tailored properties for solar cell applications. The predictive models were based on molecular descriptors that allowed us to link the structure of these materials to their performance. A series of high-performance molecules were identified from minimal suggestions and achieved up to 26.2% (certified 25.9%) power conversion efficiency in perovskite solar cells.
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