Machine learning bandgaps of double perovskites
Open accessAbstract
The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.
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Pilania et al. tackled melting temperatures of the octet subset of AB solids and band gaps of double perovskites.
“Pilania et al.210 tackled melting temperatures of the octet subset of AB solids and band gaps of double perovskites.”
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- Authors
- Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., & Lookman, T.
- Affiliations
- Los Alamos National Laboratory (US)
- University of Connecticut (US)
- Corresponding author
- Pilania, G.
- Venue
- Scientific Reports
- Published
- 2016-01-19
- Volume
- 6
- Issue
- 1
- Pages
- 19375-19375
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4726030
- OpenAlex
- W2342831537
- Cited by
- 518
- References
- 3
- Citations by year
- 2026: 47 · 2025: 48 · 2024: 51 · 2023: 45 · 2022: 72 · 2021: 57 · 2020: 71 · 2019: 56 · 2018: 37 · 2017: 26 · 2016: 8
- FWCI
- 18.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- Globus Services
- Bulletin of the American Physical Society
- Funders
- U.S. Department of Energy (DE-AC52-06NA2-5396, AC52-06NA25396, contract DE-AC52-06NA25396)
- Office of Science (DE-AC52-06NA25396)
- National Nuclear Security Administration (DEAC52- 06NA25396)
- Basic Energy Sciences (DE‐AC52‐06NA25396, AC52-06NA25396)
- Laboratory Directed Research and Development (DE-AC52-06NA2-5396)
- Los Alamos National Laboratory (AC52-06NA25396, DE-AC52-06NA25396)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Perovskite Materials and Applications
- Keywords
- double perovskite, feature engineering, machine learning, band gap prediction, perovskite oxides
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