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.
Organic photodetectors (OPDs) are promising for applications in flexible electronics due to their advantages of excellent photodetection performance, cost-effective solution-fabrication capability, flexible device design, and adaptivity to manufacturability. This review outlines the recent advances in the development of high-performance OPDs and their applications in flexible electronics. The approaches to developing different noise reduction methods, filter-free spectral selective detection, flexible OPDs, and scale-up production of flexible OPDs through solution-fabrication processes are discussed. Applications of the OPD technology ultimately result in the materialization of wearable units, flexible and compact information sensors at commercially viable costs, including wearable health self-monitoring devices, flexible optical communication systems, and flexible large-area image sensors.
Organic field‐effect transistors (OFETs) have received considerably more attention than inorganic‐based field‐effect transistors for use in next generation of organic circuits. There are a number of variables, for example, the ordering of the OFETs, their energy levels, and the material used for source/drain electrodes, that influence the magnitude of charge transport mobility. Importantly, a suitable energy level match between highest occupied molecular orbital (HOMO) or lowest unoccupied molecular orbital (LUMO) energy level and work function of the electrodes may have a large influence on the measured mobility. An informatics approach, specifically use of machine learning, is proposed for charge transport mobility prediction. Gradient Boosting and Random Forest regression algorithms are used to model previous experimental datasets and HOMO and LUMO energy levels of n‐type materials are optimized using expected machine‐learning methods. The results reveal that Random Forest model benefits the functional analysis of n‐type OFETs in three ways: 1) it provides better understanding of current n‐type organic materials, 2) it may guide the choice of n‐type organic materials and conducting electrodes, and 3) it measures the tradeoffs between the charge transport mobility and electronic energy levels for n‐type OFETs.
Organic solar cells (OSCs) based on tandem configuration have been drastically studied for boosting power conversion efficiency (PCE) in the the past decade and are a potential improvement to current commercial photovoltaic solar cell technology. Although a series of promising efficiency achievements on tandem OSCs have been reported, the crucial issue is how to estimate tandem OSCs performance based on known physical properties of different photoactive materials. Herein, a set of electronic features of photovoltaic materials is trained using machine‐learning algorithms for accurate efficiency predictions of tandem OSCs. The well‐trained machine‐learning model presented herein aims at 1) designing the matching band structures of active blends in each sub‐cell and 2) identifying the characteristics of the materials to be used to achieve high PCE values. The machine‐learning approach provides an effective strategy for suggesting the ideal properties of photovoltaic materials in terms of highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and bandgap, which is useful for the rational design of high‐efficiency tandem OSCs.
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