Machine-Learning-Assisted Screening of Interface Passivation Materials for Perovskite Solar Cells
Source abstract
Abstract
Interface passivation using an ammonium salt can effectively improve the power conversion efficiency (PCE) of perovskite solar cells (PSCs). Despite significant PCE improvement achieved in previous studies, the selection criteria for ammonium salts are not fully understood. Here we apply a machine-learning (ML) method to investigate the relationship between the molecular features of ammonium salts and the PCE improvement of PSCs. We establish an ML model using an experimental data set of 19 salts to predict the PCE improvement after passivation. Three molecular features (hydrogen bond donor, hydrogen atom, and octane–water partition coefficient) are identified as the most important features of selecting an ammonium salt for passivation. The ML model is further used to screen ammonium salts from a pool of 112 salts in the PubChem database. FAMACs and FAMA-based PSCs fabricated with a model-recommended salt (2-phenylpropane-1-aminium iodide) achieve PCEs of 22.36% and 24.47%, respectively.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Perovskite Materials and Applications · Chalcogenide Semiconductor Thin Films · Quantum Dots Synthesis And Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi 710072 ,People’s Republic of China
State Key Laboratory for Solidification Processing, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, People’s Republic of China
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 United States
NPU-Chongqing Innovation Center, Chongqing 401147, People’s Republic of China
Funding
Ministry of Science and Technology of the People's Republic of China · Central University Basic Research Fund of China · National Natural Science Foundation of China · Natural Science Foundation of Shaanxi Province · Shaanxi Bureau of Science and Technology
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