Abstract
This work presents a machine learning approach to predict novel perovskite oxide materials in the Pr-Al-O and Pr-Sc-O compound families with the potential for photoluminescence applications. The predicted materials exhibit a large bandgap and high Debye temperature, and have remained unexplored thus far. The predicted compounds (Pr\(_3\)AlO\(_6\), Pr\(_4\)Al\(_2\)O\(_9\), Pr\(_3\)ScO\(_6\) and Pr\(_3\)Sc\(_5\)O\(_{12}\)) are screened using machine learning approach, which are then confirmed by density functional theory calculations. The study includes the calculation of the bandgap and density of states to determine electronic properties, and the optical absorption and emission spectra to determine optical properties. Mechanical stability of the predicted compounds, as demonstrated by satisfying the Born-Huang criterion. By combining machine learning and density functional theory, this work offers a more efficient and comprehensive approach to materials discovery and design.
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References
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Acknowledgements
This study was supported by National R &D Program through the National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT (and RS-2023-00209910) and Virtual Engineering Platform Project (Grant No. P0022336), funded by the Ministry of Trade, Industry & Energy (MoTIE, South Korea). Upendra Kumar expresses sincere gratitude to Dr. Sanjay Nayak, a Senior Scientist at Silicon Austria Labs (SAL), for providing valuable motivation and inspiration to pursue work in the field of machine learning. Sobhit Singh (SS) was supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, Quantum Information Science program under Award Number DE-SC-0020340. SS also acknowledges support from the University Research Awards at the University of Rochester.
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Upendra Kumar and Hyeon Woo Kim conceived the idea and contributed equally to this project. Sobhit Singh provided key suggestions. Upendra Kumar wrote the manuscript and all authors read and reviewed it. Sung Beom Cho and Hyunseok Ko supervised the project.
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Appendix
Appendix
Mechanical properties
The elastic tensor matrices for Pr\(_3\)AlO\(_6\) has form:
In the case of Pr\(_3\)AlO\(_6\), which is part of the orthorhombic crystal system, the essential conditions are as follows:
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1.
\(\text{C}_{11} > 0\)
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2.
\(\text{C}_{11}\times \text{C}_{22} > \text{C}_{12}^2\)
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3.
\(\text{C}_{11}\times \text{C}_{22}\times \text{C}_{33} \ + \ 2\text{C}_{12}\times \text{C}_{13}\times \text{C}_{23} \ - \ \text{C}_{11}\times \text{C}_{23}^2 \ - \ \text{C}_{22}\times \text{C}_{13}^2 \ - \text{C}_{33}\times \text{C}_{12}^2 > 0\)
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4.
\(\text{C}_{44} > 0\)
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5.
\(\text{C}_{55} > 0\)
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6.
\(\text{C}_{66} > 0\)
The elastic tensor matrices for Pr\(_4\)Al\(_2\)O\(_9\) has form:
For Pr\(_4\)Al\(_2\)O\(_9\) which belongs to the monoclinic crystal system, the necessary criteria are given as:
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1.
C\(_{11}>\) 0
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2.
C\(_{22}>\) 0
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3.
C\(_{33}>\) 0
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4.
C\(_{44}>\) 0
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5.
C\(_{55}>\)
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6.
C\(_{66}>\) 0
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7.
\(\big [\)C\(_{11}\) + C\(_{22}\) + C\(_{33}\) + 2\(\times\)(C\(_{12}\) + C\(_{13}\) + C\(_{23}\))\(\big ]>\) 0
-
8.
C\(_{33}\times\)C\(_{55}\) - C\(_{35}^2>\) 0
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9.
C\(_{44}\times\)C\(_{66}\) - C\(_{46}^2>\) 0 (x) C\(_{22}\) + C\(_{33}\) - 2\(\times\)C\(_{23}\) > 0
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10.
C\(_{22}\times\)(C\(_{33}\times\)C\(_{55}\) - C\(_{35}^2\)) + 2\(\times\)C\(_{23}\times\)C\(_{25}\times\)C\(_{35}\) - (C\(_{23}\))\(^2\times\)C\(_{55}\) - (C\(_{25}\))\(^2\times\)C\(_{33}\) > 0
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11.
2\(\times\)[C\(_{15}\times\)C\(_{25}\times\)(C\(_{33}\times\)C\(_{12}\) - C\(_{13}\times\)C\(_{23}\)) + C\(_{15}\times\)C\(_{35}\times\)(C\(_{22}\times\)C\(_{13}\) - C\(_{12}\times\)C\(_{23}\)) + C\(_{25}\times\)C\(_{35}\times\)(C\(_{11}\times\)C\(_{23}\) - C\(_{12}\times\)C\(_{13}\))] - [C\(_{15}\times\)C\(_{15}\times\)(C\(_{22}\times\)C\(_{33}\) - C\(_{23}^2\)) + C\(_{25}\times\)C\(_{25}\times\)(C\(_{11}\times\)C\(_{33}\) - C\(_{13}^2\)) + C\(_{35}\times\)C\(_{35}\times\)(C\(_{11}\times\)C\(_{22}\) - C\(_{12}^2\))] + C\(_{55}\times\)g > 0 where, g = [C\(_{11}\times\)C\(_{22}\times\)C\(_{33}\) - C\(_{11}\times\)C\(_{23}\times\)C\(_{23}\) - C\(_{22}\times\)C\(_{13}\times\)C\(_{13}\) - C\(_{33}\times\)C\(_{12}\times\)C\(_{12}\) + 2\(\times\)C\(_{12}\times\)C\(_{13}\times\)C\(_{23}\) ].
The elastic tensor matrices for Pr\(_3\)ScO\(_6\) has form:
For Pr\(_3\)ScO\(_6\) which belongs to the rhombohedral-2 crystal system, the necessary criteria are given as;
-
1.
C\(_{11}\) - C\(_{12}\) > 0
-
2.
C\(_{13}^2 < (1/2)\times\)C\(_{33}\)(C\(_{11}\) + C\(_{12}\))
-
3.
C\(_{14}^2\) + C\(_{15}^2 < (1/2)\times\)C\(_{44}\times\)(C\(_{11}\)-C\(_{12}\)) = C\(_{44}\times\)C\(_{66}\)
-
4.
C\(_{44}\) > 0
The elastic tensor matrices for Pr\(_3\)Sc\(_5\)O\(_{12}\) has form:
For Pr\(_3\)Sc\(_5\)O\(_{12}\) which belongs to the cubic crystal system, the necessary criteria are given as;
-
1.
C\(_{11}\) - C\(_{12}\) > 0
-
2.
C\(_{11}\) + 2C\(_{12}\) > 0
-
3.
C\(_{44}\) > 0
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Kumar, U., Kim, H.W., Singh, S. et al. Designing Pr-based advanced photoluminescent materials using machine learning and density functional theory. J Mater Sci 59, 1433–1447 (2024). https://doi.org/10.1007/s10853-023-09232-6
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DOI: https://doi.org/10.1007/s10853-023-09232-6