Machine learning assisted identification of the matched energy level of materials for high open circuit voltage in binary organic solar cells
Kuo Wang, Chaorong Guo, Zhennan Li, Rui Zhang, Zhimin Feng, Gengkun Fang, Di Huang, Jiaojiao Liang, Ling Zhao, Zicha Li
Abstract
The effects of the materials' energy levels on the V oc in binary OSCs are analyzed and the energy level matching strategy of materials for high V oc is delivered by machine learning. Experimental results verify the reliability of this machine learning approach.
Topics & Concepts
Reliability (semiconductor)Identification (biology)Binary numberEnergy (signal processing)Open-circuit voltageVoltageMaterials scienceComputer scienceArtificial intelligenceOptoelectronicsElectrical engineeringEngineeringPhysicsPower (physics)MathematicsStatisticsQuantum mechanicsArithmeticBiologyBotanyOrganic Electronics and PhotovoltaicsConducting polymers and applicationsThin-Film Transistor Technologies