钙钛矿(结构)
半导体
反向
材料科学
分子
光电子学
工程物理
纳米技术
物理
化学
结晶学
数学
量子力学
几何学
作者
Jianchang Wu,Luca Torresi,Manman Hu,Patrick Reiser,Jiyun Zhang,Juan S. Rocha‐Ortiz,Lianzhou Wang,Zhiqiang Xie,Kaicheng Zhang,Byung‐wook Park,Anastasia Barabash,Yicheng Zhao,Junsheng Luo,Yunuo Wang,Larry Lüer,Lin‐Long Deng,Jens Hauch,Sang Il Seok,Pascal Friederich,Christoph J. Brabec
出处
期刊:Cornell University - arXiv
日期:2024-06-30
标识
DOI:10.48550/arxiv.2407.00729
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 realized1,2. The complexity and literally infinite diversity of conjugated molecular structures present both, an unprecedented opportunity for technological breakthroughs as well as an unseen optimization challenge. Current models rely on big data which do not exist for specialized research films. However, a hybrid computational and high throughput experimental screening workflow allowed us to train predictive models with as little as 149 molecules. We demonstrate a unique closed-loop workflow combining high throughput synthesis and Bayesian optimization that discovers new hole transporting materials with tailored properties for solar cell applications. A series of high-performance molecules were identified from minimal suggestions, achieving up to 26.23% (certified 25.88%) power conversion efficiency in perovskite solar cells. Our work paves the way for rapid, informed discovery in vast molecular libraries, revolutionizing material selection for complex devices. We believe that our approach can be generalized to other emerging fields and indeed accelerate the development of optoelectronic semiconductor devices in general.
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