Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning
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References 81 · Cited by 33
Research works printed but not linked: 4
- [10]10. Yan, Y., Yin, W.-J., Shi, T., Meng, W. & Feng, C. Defect Physics of CH 3 NH 3 PbX3 (X = I, Br, Cl) Perovskites, 79-105 (Springer International Publishing, Cham, 2016).
- [62]62. Jiang, Z. et al. Special quasirandom structures for perovskite solid solutions. Journal of Physics: Condensed Matter 28, 475901 (2016).
- [82]82. Diez, M. Porosity Optimization in Nanoporous materials via Machine Learning. Master's thesis, Polytechnic University of Turin.
- [85]85. Williams, C. K. & Rasmussen, C. E. Gaussian processes for machine learning, vol. 2 (MIT press Cambridge, MA, 2006).
- Authors
- Mannodi-Kanakkithodi, A., & Chan, M. K. Y.
- Affiliations
- Argonne National Laboratory (US)
- Venue
- Journal of Materials Science
- Published
- 2022-03-04
- Volume
- 57
- Issue
- 23
- Pages
- 10736-10754
- ISSN
- 0022-2461 (ISSN-L)
- 1573-4803
- OpenAlex
- W4214936575
- Cited by
- 33
- References
- 81
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 7 · 2023: 7 · 2022: 2
- FWCI
- 1.83
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- Journal of Materials Science (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy
- Purdue University
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Perovskite Materials and Applications
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- halide perovskites, methylammonium lead halide, impurity atoms, formation energy, charge transition levels, native point defects, Fermi level shift, impurity screening, Gaussian process regression, artificial neural networks, random forest, optoelectronic properties, charge recombination
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