以昇陳

Machine‐learning approaches are utilized to build models for the prediction of efficiency using important frontier molecular orbital energy levels of organic materials as features. Furthermore, a versatile Random Forest model reveals that the lowest unoccupied molecular orbital energy of donor can be considered as a critical feature in design of ternary organic solar cells.

Abstract

Ternary organic solar cells (OSCs) have progressed significantly in recent years due to the sufficient photon harvesting of the blend photoactive layer including three absorption‐complementary materials. With the rapid development of highly efficient ternary OSCs in photovoltaics, the precise energy‐level alignment of the three active components within ternary OSC devices should be taken into account. The machine‐learning technique is a computational method that can effectively learn from previous historical data to build predictive models. In this study, a dataset of 124 fullerene derivatives‐based ternary OSCs is manually constructed from a diverse range of literature along with their frontier molecular orbital theory levels, and device structures. Different machine‐learning algorithms are trained based on these electronic parameters to predict photovoltaic efficiency. Thus, the best predictive capability is provided by using the Random Forest approach beyond other machine‐learning algorithms in the dataset. Furthermore, the Random Forest algorithm yields valuable insights into the crucial role of lowest unoccupied molecular orbital energy levels of organic donors in the performance of ternary OSCs. The outcome of this study demonstrates a smart strategy for extracting underlying complex correlations in fullerene derivatives‐based ternary OSCs, thereby accelerating the development of ternary OSCs and related research fields.

Discovers this issue’s cover story. High quality research handpicked by the Editor-in-Chief. This cover schematizes efficient ternary PSCs and their potential application in powering high-speed trains as a flexible, light, and green energy source for better serving the Belt and Road Initiative. Read more.

Machine‐learning approaches are utilized to build models for the prediction of efficiency using important frontier molecular orbital energy levels of organic materials as features. Furthermore, a versatile Random Forest model reveals that the lowest unoccupied molecular orbital energy of donor can be considered as a critical feature in design of ternary organic solar cells.

Abstract

Ternary organic solar cells (OSCs) have progressed significantly in recent years due to the sufficient photon harvesting of the blend photoactive layer including three absorption‐complementary materials. With the rapid development of highly efficient ternary OSCs in photovoltaics, the precise energy‐level alignment of the three active components within ternary OSC devices should be taken into account. The machine‐learning technique is a computational method that can effectively learn from previous historical data to build predictive models. In this study, a dataset of 124 fullerene derivatives‐based ternary OSCs is manually constructed from a diverse range of literature along with their frontier molecular orbital theory levels, and device structures. Different machine‐learning algorithms are trained based on these electronic parameters to predict photovoltaic efficiency. Thus, the best predictive capability is provided by using the Random Forest approach beyond other machine‐learning algorithms in the dataset. Furthermore, the Random Forest algorithm yields valuable insights into the crucial role of lowest unoccupied molecular orbital energy levels of organic donors in the performance of ternary OSCs. The outcome of this study demonstrates a smart strategy for extracting underlying complex correlations in fullerene derivatives‐based ternary OSCs, thereby accelerating the development of ternary OSCs and related research fields.

The rapid development of n‐type polymers has boosted the efficiency of all‐polymer solar cells, which has improved from 2% to 10% in only seven years. There is a strong need to summarize the design criteria, synthesis, structure–property relationships and recent advances of n‐type polymers, which is addressed in this review. Moreover, the challenges and prospects for further development of all‐PSCs are briefly discussed.

Abstract

All‐polymer solar cells (all‐PSCs) based on n‐ and p‐type polymers have emerged as promising alternatives to fullerene‐based solar cells due to their unique advantages such as good chemical and electronic adjustability, and better thermal and photochemical stabilities. Rapid advances have been made in the development of n‐type polymers consisting of various electron acceptor units for all‐PSCs. So far, more than 200 n‐type polymer acceptors have been reported. In the last seven years, the power conversion efficiency (PCE) of all‐PSCs rapidly increased and has now surpassed 10%, meaning they are approaching the performance of state‐of‐the‐art solar cells using fullerene derivatives as acceptors. This review discusses the design criteria, synthesis, and structure–property relationships of n‐type polymers that have been used in all‐PSCs. Additionally, it highlights the recent progress toward photovoltaic performance enhancement of binary, ternary, and tandem all‐PSCs. Finally, the challenges and prospects for further development of all‐PSCs are briefly considered.

Protonation of polyethylenimine ethoxylated (PEIE) can effectively passivate the chemical reaction between the PEIE and a nonfullerene (NF) active layer. As a result, the PEIE can work very efficiently as a low‐work‐function interface for NF solar cells. These flexible solar cells exhibit power conversion efficiency up to 12.5% with a room‐temperature‐processed PEIE interface.

Abstract

Nonfullerene (NF) organic solar cells (OSCs) have been attracting significant attention in the past several years. It is still challenging to achieve high‐performance flexible NF OSCs. NF acceptors are chemically reactive and tend to react with the low‐temperature‐processed low‐work‐function (low‐WF) interfacial layers, such as polyethylenimine ethoxylated (PEIE), which leads to the “S” shape in the current‐density characteristics of the cells. In this work, the chemical interaction between the NF active layer and the polymer interfacial layer of PEIE is deactivated by increasing its protonation. The PEIE processed from aqueous solution shows more protonated N⁺ than that processed from isopropyl alcohol solution, observed from X‐ray photoelectron spectroscopy. NF solar cells (active layer: PCE‐10:IEICO‐4F) with the protonated PEIE interfacial layer show an efficiency of 13.2%, which is higher than the reference cells with a ZnO interlayer (12.6%). More importantly, the protonated PEIE interfacial layer processed from aqueous solution does not require a further thermal annealing treatment (only processing at room temperature). The room‐temperature processing and effective WF reduction enable the demonstration of high‐performance (12.5%) flexible NF OSCs.

Symmetry breaking provides a new material design strategy for nonfullerene small molecule acceptors (SMAs). The past 10 years have witnessed significant advances in asymmetric nonfullerene SMAs in organic solar cells (OSCs). In this review, the progress of asymmetric nonfullerene SMAs is reviewed. The structure–property relationships and the perspectives for future development of asymmetric non‐fullerene SMAs are also discussed.

Abstract

Symmetry breaking provides a new material design strategy for nonfullerene small molecule acceptors (SMAs). The past 10 years have witnessed significant advances in asymmetric nonfullerene SMAs in organic solar cells (OSCs) with power conversion efficiency (PCE) increasing from ≈1% to ≈14%. In this review, the progress of asymmetric nonfullerene SMAs, including early reports of asymmetric nonfullerene SMAs, asymmetric PDI‐based nonfullerene SMAs, and asymmetric acceptor–donor–acceptor (A–D–A)‐type nonfullerene SMAs, is summarized. The structure–property relationships and the perspectives for future development of asymmetric nonfullerene SMAs are also discussed.