有机太阳能电池
轨道能级差
接受者
偏移量(计算机科学)
光伏系统
材料科学
计算机科学
人工智能
物理
分子
工程类
电气工程
量子力学
程序设计语言
出处
期刊:Solar RRL
[Wiley]
日期:2023-07-18
卷期号:7 (18)
被引量:13
标识
DOI:10.1002/solr.202300533
摘要
An efficient computational approach, in contrast to the trial‐and‐error experiment process, for predicting, characterizing, and optimizing the macroscopic performance parameters (e.g., short‐circuit current density (Jsc)) of nonfullerene acceptors‐based organic solar cells (OSCs) remains a rarely addressed and complicated challenge. In this work, a data‐driven approach is used to predict the electrical performance of nonfullerene OSCs and reveal their charge transfer behaviors. The eXtreme Gradient Boosting (XGBoost) model within empirical descriptors is used to understand the governing feature for enhancement of Jsc, which is vital for the design and discovery of new donor/nonfullerene acceptor photoactive layers for photovoltaic applications. Through the well‐trained XGBoost model and SHapley Additive exPlanations theory, the descriptors impacting the Jsc of nonfullerene OSCs are further explained and analyzed. Remarkably, the XGBoost model combines four empirical descriptors to achieve an impressive prediction accuracy (R2 > 0.8). The results from data‐driven approaches prove that the lowest unoccupied molecular orbital (LUMO) offset (between donor and acceptor) plays the most significant role in increasing the Jsc values of nonfullerene OSCs. Moreover, this study highlights the effect of LUMO offset on the photoinduced charge transfer process of donor/non‐fullerene acceptor blends, which might pave the way toward rapid and precise energy‐level tuning of efficient OSC materials.
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