工作流程
贝叶斯优化
钙钛矿(结构)
反向
能量转换效率
材料科学
贝叶斯概率
有机半导体
功率(物理)
有机太阳能电池
计算机科学
纳米技术
分布式计算
光电子学
化学
物理
聚合物
人工智能
数据库
数学
几何学
量子力学
复合材料
结晶学
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
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2024-12-12
卷期号:386 (6727): 1256-1264
被引量:16
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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