We use an automated research platform combined with machine learning to assess and understand the resilience against air and light during production of organic photovoltaic(OPV)devices from over 40 donor and acceptor ...We use an automated research platform combined with machine learning to assess and understand the resilience against air and light during production of organic photovoltaic(OPV)devices from over 40 donor and acceptor combina-tions.The standardized protocol and high reproducibility of the platform results in a dataset of high variety and veracity to deploy machine learning models to encounter links between stability and chemical,energetic,and mor-phological structure.We find that the strongest predictor for air/light resilience during production is the effective gap Eg,eff which points to singlet oxygen rather than the superoxide anion being the dominant agent in degradation under processing conditions.A similarly good prediction of air/light resilience can also be achieved by considering only features from chemical structure,that is,information which is available prior to any experimentation.展开更多
基金The authors want to thank the Deutsche Forschungsge-meinschaft(DFG)for financial support in scope of the DFG INST 90/917-1 FUGGof the projects FKZ BR 4031/21-1 and BR 4031/22-1+6 种基金We also gratefully acknowledge the grants“ELF-PV-Design and development of solution processed functional materials for the next generations of PV technologies”(No.44-6521a/20/4)“Solar Factory of the Future”(FKZ 20.2-3410.5-4-5)by the Bavarian State GovernmentC.J.B.gratefully acknowledges the financial support through the Bavarian Initiative“Solar Technologies go Hybrid”(SolTech)and the SFB 953(DFG,project No.182849149)N.L.and C.J.B.acknowledge the financial support by the DFG research unit project“POPULAR”(FOR 5387,project no.461909888)X.D.thanks Natural Science Foundation of Shandong Province(2022HWYQ-012)N.L.acknowledges the financial support by the National Natural Science Foundation of China(52394273,52373179)L.Ding thanks the National Key Research and Development Program of China(2023YFE0116800)for financial support.
文摘We use an automated research platform combined with machine learning to assess and understand the resilience against air and light during production of organic photovoltaic(OPV)devices from over 40 donor and acceptor combina-tions.The standardized protocol and high reproducibility of the platform results in a dataset of high variety and veracity to deploy machine learning models to encounter links between stability and chemical,energetic,and mor-phological structure.We find that the strongest predictor for air/light resilience during production is the effective gap Eg,eff which points to singlet oxygen rather than the superoxide anion being the dominant agent in degradation under processing conditions.A similarly good prediction of air/light resilience can also be achieved by considering only features from chemical structure,that is,information which is available prior to any experimentation.
