Abstract Dimethylammonium lead iodide (DMAPbIx) has the potential to address the phase stability issue of inorganic perovskite solar cells (PSCs). In this study, the crystallinity, phase structure, defect states, and crystal growth habits of DMAPbIxare controlled by adjusting thexvalue during synthesis, where N,N‐dimethylacetamide (DMAC) is used as the solvent to regulate perovskite film growth. Furthermore, large‐area CsPbI2.85Br0.15perovskite films with preferred oriented growth are achieved using the optimizedxvalue in DMAPbIxthrough the slot‐die coating method. The inorganic PSCs, with a n‐i‐p structure and the active area of 0.04 cm2, achieve a champion power conversion efficiency (PCE) of 19.82%, with an open‐circuit voltage (Voc) of 1.16 V based on perovskite films formed by slot‐die coating. This work provides important insights into the DMAPbIx‐based method for fabricating high‐quality inorganic perovskite films, and paves the way for large‐area inorganic PSCs fabrication for practical applications.
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Machine Learning for Perovskite Solar Cells: An Open‐Source Pipeline
Abstract Among promising applications of metal‐halide perovskite, the most research progress is made for perovskite solar cells (PSCs). Data from myriads of research work enables leveraging machine learning (ML) to significantly expedite material and device optimization as well as potentially design novel configurations. This paper represents one of the first efforts in providing open‐source ML tools developed utilizing the Perovskite Database Project (PDP), the most comprehensive open‐source PSC database to date with over 43 000 entries from published literature. Three ML model architectures with short‐circuit current density (Jsc) as a target are trained exploiting the PDP. Using the XGBoost architecture, a root mean squared error (RMSE) of 3.58 , R2of 0.35 and a mean absolute percentage error (MAPE) of 9.49% are achieved. This performance is comparable to results reported in literature, and through further investigation can likely be improved. To overcome challenges with manual database creation, an open‐source data cleaning pipeline is created for PDP data. Through the creation of these tools, which have been published on GitHub, this research aims to make ML available to aid the design for PSC while showing the already promising performance achieved. The tools can be adapted for other applications, such as perovskite light‐emitting diodes (PeLEDs), if a sufficient database is available.
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- Award ID(s):
- 2227285
- PAR ID:
- 10516235
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Physics Research
- Volume:
- 3
- Issue:
- 11
- ISSN:
- 2751-1200
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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