The generation of a database of promising short-circuit current organic solar cell hole transport layers with machine learning and density functional theory

Key Points

• This research aims to develop a database of promising materials for hole transport layers in organic solar cells.
• Utilized machine learning techniques to integrate generative design.
• Applied density functional theory for material analysis.
• Generated a comprehensive database of potential hole transport layers.
• Successfully identified novel hole transport layers for organic solar cells.
• Demonstrated improved efficiency in discovering new materials compared to traditional QSPR methods.