三元运算
接受者
有机太阳能电池
计算机科学
人工智能
材料科学
生物系统
机器学习
光伏系统
物理
工程类
凝聚态物理
生物
电气工程
程序设计语言
出处
期刊:Solar RRL
[Wiley]
日期:2023-06-06
卷期号:7 (14)
被引量:3
标识
DOI:10.1002/solr.202300307
摘要
The challenge of accurately predicting the power conversion efficiency (PCE) of ternary organic solar cells (OSCs) based on a nonfullerene acceptor holds the key to the rational design of a ternary blend. Developing an effective descriptor with experimentally measurable and theoretically computable signatures for accurately predicting the PCE of OSCs based on nonfullerene acceptors is an important step toward achieving this goal. Herein, the electronegativity is first proposed as an effective molecular descriptor for predicting the PCE of OSCs based on nonfullerene acceptors and further analyzing the underlying relationships between material property and device performance. Remarkably, the high accuracy (Coefficient of Determination) > 0.9) can be achieved by constructing the machine learning model with a fewer number of descriptors. In addition, the SHapley Additive exPlanations approach is introduced to provide both local and global interpretations for extracting a deep understanding of complex molecular descriptor–PCE relationships. These results in this study validate the effectiveness of the molecular descriptor, providing an efficient modality for rapid and precise screening of high‐performance ternary materials.
科研通智能强力驱动
Strongly Powered by AbleSci AI