Abstract
Pseudo-halide (PH) anion engineering has emerged as a surface passivation strategy of interest for perovskite-based optoelectronics; but until now, PH anions have led to insufficient defect passivation and thus to undesired deep impurity states. The size of the chemical space of PH anions (>106 molecules) has so far limited attempts to explore the full family of candidate molecules. We created a machine learning workflow to speed up the discovery process using full-density functional theory calculations for training the model. The physics-informed machine learning model allowed us to pinpoint promising molecules with a head group that prevents lattice distortion and anti-site defect formation, and a tail group optimized for strong attachment to the surface. We identified 15 potential bifunctional PH anions with the ability to passivate both donors and acceptors, and through experimentation, discovered that sodium thioglycolate was the most effective passivant. This strategy resulted in a power-conversion efficiency of 24.56% with a high open-circuit voltage of 1.19 volts (24.04% National Renewable Energy Lab-certified quasi-steady-state) in inverted perovskite solar cells. Encapsulated devices maintained 96% of their initial power-conversion energy during 900 hours of one-sun operation at the maximum power point.
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Data availability
The main data supporting the findings of this study are available within the Article and its Supplementary Information.
Code availability
We include the codes for the materials screening procedure in Supplementary Note 11 as well as the codes for preprocessing and ML model construction in Supplementary Note 12. The Vienna Ab initio Simulation Package code for the numerical simulations in this work can be found at https://www.vasp.at; the Gaussian code can be found at https://gaussian.com/; the Multiwfn code can be found at http://sobereva.com/multiwfn/; the scikit-learn is available at https://scikit-learn.org/; the Matplotlib is available at https://matplotlib.org.
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Acknowledgements
This research was made possible by King Abdullah University of Science and Technology Office of Sponsored Research under award no. OSR-2020-CRG9-4350.2 and by the US Department of the Navy, Office of Naval Research Grant (N00014-20-1-2572 (E.H.S.) and N00014-20-1-2725 (M.G.K.)). SciNet is funded by the Canada Foundation for Innovation under the auspices of Compute Canada. We thank W. Zhou for his contribution in independently verifying the impact of the ST treatment on PSC performance, under the supervision of Z. Ning from Shanghai Tech University.
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Contributions
J.X. conceived the idea. J.X., H.C., L.G. and E.H.S. designed the project. J.X. performed all DFT calculations and ML. H.C. fabricated the devices. L.G. and S.T. performed PL characterization. C.L. and Y.Y. carried out XPS, sputtering XPS and SEM characterization under the supervision of M.G.K. J.X. and Y.Y. analysed the XPS results. L.G. analysed the sputtering XPS and SEM results. A.M. fabricated the narrow-bandgap perovskite films. H.W. conducted the XRD measurement. S.M. and Y.C. participate in the ML aspect. J.X., L.G. and E.H.S. wrote the manuscript. B.C., B.R., S.M.P. and M.G.K. improved the manuscript. All authors discussed the results and commented on the paper.
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Nature Materials thanks Jin Young Kim, Thierry Pauporte and Lei Zhang for their contribution to the peer review of this work.
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Supplementary Information
Supplementary Figs. 1–34, Tables 1–2 and Notes 1–12.
Supplementary Table 1
Dataset for ML.
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Xu, J., Chen, H., Grater, L. et al. Anion optimization for bifunctional surface passivation in perovskite solar cells. Nat. Mater. 22, 1507–1514 (2023). https://doi.org/10.1038/s41563-023-01705-y
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DOI: https://doi.org/10.1038/s41563-023-01705-y
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