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Revealing processing stability landscape of organic solar cells with automated research platforms and machine learning

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InfoMat 2024年第7期6卷 51-61页

作者： Xiaoyan Du Larry Lüer Thomas Heumueller Andrej Classen Chao Liu Christian Berger Jerrit Wagner Vincent M.Le Corre Jiamin Cao Zuo Xiao Liming Ding Karen Forberich Ning Li jens hauch Christoph J.Brabec Helmholtz Institute Erlangen‐Nürnberg for Renewable Energy(HI ERN) Forschungszentrum Jülich GmbHErlangenGermany Institute of Materials for Electronics and Energy Technology(i‐MEET) Friedrich‐Alexander‐Universität Erlangen‐Nürnberg(FAU)ErlangenGermany School of Physics State Key Laboratory of Crystal MaterialsShandong UniversityJinanthe People’s Republic of China Key Laboratory of Theoretical Organic Chemistry and Functional Molecule of Ministry of Education School of Chemistry and Chemical EngineeringHunan University of Science and TechnologyXiangtanthe People’s Republic of China Center for Excellence in Nanoscience Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijingthe People’s Republic of China Institute of Polymer Optoelectronic Materials&Devices Guangdong Basic Research Center of Excellence for Energy&Information Polymer MaterialsState Key Laboratory of Luminescent Materials&DevicesSouth China University of Technology

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 *** 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 *** 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.

关键词： air stability automated screening donor/acceptor combinations machine learning organic solar cells

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Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics

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Materials Genome Engineering Advances 2024年第1期2卷 38-63页

作者： Ying Shang Ziyu Xiong Kang An jens A.hauch Christoph J.Brabec Ning Li Institute of Polymer Optoelectronic Materials and Devices State Key Laboratory of Luminescent Materials and Devices South China University of TechnologyGuangzhouChina Helmholtz-Institute Erlangen-Nürnberg(HI ERN) ErlangenGermany Institute of Materials for Electronics and Energy Technology(i-MEET) Friedrich-Alexander-UniversitätErlangen-NürnbergErlangenGermany Guangdong Basic Research Center of Excellence for Energy&Information Polymer Materials South China University of TechnologyGuangzhouChina

The emerging photovoltaic(PV)technologies,such as organic and perovskite PVs,have the characteristics of complex compositions and processing,resulting in a large multidimensional parameter space for the development and optimization of the *** manual methods are time-consuming and laborintensive in screening and optimizing material *** genome engineering(MGE)advances an innovative approach that combines efficient experimentation,big database and artificial intelligence(AI)algorithms to accelerate materials research and ***-throughput(HT)research platforms perform multidimensional experimental tasks rapidly,providing a large amount of reliable and consistent data for the creation of materials ***,the development of novel experimental methods combining HT and AI can accelerate materials design and application,which is beneficial for establishing material-processing-property relationships and overcoming bottlenecks in the development of emerging PV *** review introduces the key technologies involved in MGE and overviews the accelerating role of MGE in the field of organic and perovskite PVs.

关键词： artificial intelligence emerging photovoltaic technology high-throughput experiment materials genome engineering organic solar cells perovskite solar cells

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