(Invited) Machine Learning and Fast Experimental Screening-Assisted Development of Organic Solar Cell

Abstract

Non-fullerene, a small molecular electron acceptor, has substantially improved the power conversion efficiency of organic photovoltaics (OPVs).[1] However, the large structural freedom of π-conjugated polymers and molecules makes it difficult to be explored with limited resources. Machine learning, which is based on the 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 review, which focuses on utilizing the experimental dataset for ML to efficiently aid OPV research. The author 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.[2] Despite the advance of ML, the predictive accuracy of ML currently remains insufficient for the design of OPV semiconductors that exhibit a complex connectivity between chemical structure and PCE. In this study, we examined the impact of data selection and the introduction of artificially generated failure data on ML predictions of NFA solar cells. The authors 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.[3] 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 ML-driven development of OPV materials that is also readily applicable to other material science fields. Reference [1] Kranthiraja, A. Saeki, Adv. Funct. Mater. 31 (2021) 2011168 [2] Miyake, A. Saeki, J. Phys. Chem. Lett. 12 (2021) 12391. [3] Miyake, K. Kranthiraja, F. Ishiwari, A. Saeki, Chem. Mater. 34 (2022) 6912.