DOI: 10.1002/cphc.201800382. Density Functional Theory – Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden‐Popper Phases

Density Functional Theory – Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden‐Popper Phases

10.1002/cphc.201800382

Crossref journal-article

Wiley

ChemPhysChem (311)

Abstract

AbstractIn this study, we have developed a protocol for exploring the vast chemical space of possible perovskites and screening promising candidates. Furthermore, we examined the factors that affect the band gap energies of perovskites. The Goldschmidt tolerance factor and octahedral factor, which range from 0.98 to 1 and from 0.45 to 0.7, respectively, are used to filter only highly cubic perovskites that are stable at room temperature. After removing rare or radioactively unstable elements, quantum mechanical density functional theory calculations are performed on the remaining perovskites to assess whether their electronic properties such as band structure are suitable for solar cell applications. Similar calculations are performed on the Ruddlesden‐Popper phase. Furthermore, machine learning was utilized to assess the significance of input parameters affecting the band gap of the perovskites.

Bibliography

Allam, O., Holmes, C., Greenberg, Z., Kim, K. C., & Jang, S. S. (2018). Density Functional Theory â Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and RuddlesdenâPopper Phases. ChemPhysChem, 19(19), 2559â2565. Portico.

Authors 5

Omar Allam (first)
Colin Holmes (additional)
Zev Greenberg (additional)
Ki Chul Kim (additional)
Seung Soon Jang (additional)

References 39 Referenced 31

10.1039/C7EE00757D
10.1021/jz4020162
10.1038/nature18306
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10.1021/acs.nanolett.7b02146
10.1016/j.chempr.2017.02.015
10.1016/j.chempr.2017.02.004
10.1021/acs.chemmater.6b00847
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10.1021/acs.chemmater.5b04107
10.1016/j.commatsci.2013.12.054
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10.1016/j.jssc.2013.05.012
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10.1103/PhysRevB.16.1748
{'key': 'e_1_2_7_39_1', 'first-page': '843', 'volume': '14', 'author': 'Hennig P.', 'year': '2013', 'journal-title': 'J. Mach. Learn. Res.'} / J. Mach. Learn. Res. by Hennig P. (2013)

Dates

Type	When
Created	7 years, 2 months ago (June 21, 2018, 4:07 p.m.)
Deposited	1 year, 11 months ago (Sept. 15, 2023, 10:33 p.m.)
Indexed	4 weeks ago (July 31, 2025, 11:55 p.m.)
Issued	7 years, 1 month ago (July 5, 2018)
Published	7 years, 1 month ago (July 5, 2018)
Published Online	7 years, 1 month ago (July 5, 2018)
Published Print	6 years, 10 months ago (Oct. 5, 2018)

Funders 1

Georgia Institute of Technology 10.13039/100006778
Region: Americas
gov (Universities (academic only))
Labels5
1. Georgia Tech
2. Georgia School of Technology
3. GA Tech
4. GIT
5. GT

BibTeX

@article{Allam_2018, title={Density Functional Theory – Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden‐Popper Phases}, volume={19}, ISSN={1439-7641}, url={http://dx.doi.org/10.1002/cphc.201800382}, DOI={10.1002/cphc.201800382}, number={19}, journal={ChemPhysChem}, publisher={Wiley}, author={Allam, Omar and Holmes, Colin and Greenberg, Zev and Kim, Ki Chul and Jang, Seung Soon}, year={2018}, month=jul, pages={2559–2565} }

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