Nonfullerene,
a small molecular electron acceptor, has substantially
improved the power conversion efficiency of organic photovoltaics
(OPVs). However, the large structural freedom of π-conjugated
polymers and molecules makes it difficult to explore with limited
resources. Machine learning, which is based on rapidly growing artificial
intelligence technology, is a high-throughput method to accelerate
the speed of material design and process optimization; however, it
suffers from limitations in terms of prediction accuracy, interpretability,
data collection, and available data (particularly, experimental data).
This recognition motivates the present Perspective, which focuses
on utilizing the experimental data set for ML to efficiently aid OPV
research. This Perspective discusses the trends in ML-OPV publications,
the NFA category, and the effects of data size and explanatory variables
(fingerprints or Mordred descriptors) on the prediction accuracy and
explainability, which broadens the scope of ML and would be useful
for the development of next-generation solar cell materials.
The conventional development of organic photovoltaic (OPV) materials has been driven by the inspiration and continuous endeavors of experimentalists, whereas machine learning (ML) approaches enable extremely high-throughput data processing. However, the predictive accuracy of ML currently remains insufficient for the design of OPV semiconductors that exhibit a complex connectivity between chemical structure and power conversion efficiency (PCE). In this study, we examined the impact of data selection and the introduction of artificially generated failure data on ML predictions of polymer/non-fullerene, smallmolecular-acceptor (NFA) solar cells. We demonstrated that an ML model empowered by artificially generated failure data (∼0% PCE by insoluble polymers based on an inappropriate choice of solubilizing side alkyl chains) led to improved predictions. This approach was validated through the synthesis and characterization of twelve polymers (benzothiadiazole, thienothiophene, or tetrazine coupled with benzodithiophene; benzobisthiazole coupled with dioxo-benzodithiophene). Our work offers a facile approach to mitigate the difficulties of the MLdriven development of OPV materials that is also readily applicable to other material science fields.
We incorporated atomic force microscopy images of polymer : non-fullerene acceptor organic photovoltaics into machine learning, where fast Fourier transform and grey-level co-occurrence matrix were utilized to predict power conversion efficiencies.
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