Machine learning bandgaps of double perovskites
Abstract
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. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Univ. of Connecticut, Storrs, CT (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1235935
- Report Number(s):
- LA-UR-15-23084
Journal ID: ISSN 2045-2322; srep19375
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 6; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; computational methods; electronic; properties and materials; statistics
Citation Formats
Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., and Lookman, T. Machine learning bandgaps of double perovskites. United States: N. p., 2016.
Web. doi:10.1038/srep19375.
Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., & Lookman, T. Machine learning bandgaps of double perovskites. United States. https://doi.org/10.1038/srep19375
Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., and Lookman, T. Tue .
"Machine learning bandgaps of double perovskites". United States. https://doi.org/10.1038/srep19375. https://www.osti.gov/servlets/purl/1235935.
@article{osti_1235935,
title = {Machine learning bandgaps of double perovskites},
author = {Pilania, G. and Mannodi-Kanakkithodi, A. and Uberuaga, B. P. and Ramprasad, R. and Gubernatis, J. E. and Lookman, T.},
abstractNote = {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. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.},
doi = {10.1038/srep19375},
journal = {Scientific Reports},
number = ,
volume = 6,
place = {United States},
year = {Tue Jan 19 00:00:00 EST 2016},
month = {Tue Jan 19 00:00:00 EST 2016}
}
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- Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
- Advanced Science, Vol. 7, Issue 2
Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators
journal, February 2019
- Pilania, G.; Liu, Xiang-Yang; Wang, Zhehui
- Journal of Materials Science, Vol. 54, Issue 11
Predicting superhard materials via a machine learning informed evolutionary structure search
journal, September 2019
- Avery, Patrick; Wang, Xiaoyu; Oses, Corey
- npj Computational Materials, Vol. 5, Issue 1
A hybrid organic-inorganic perovskite dataset
journal, May 2017
- Kim, Chiho; Huan, Tran Doan; Krishnan, Sridevi
- Scientific Data, Vol. 4, Issue 1
Graph Neural Network for Hamiltonian-Based Material Property Prediction
preprint, January 2020
- Bai, Hexin; Chu, Peng; Tsai, Jeng-Yuan
- arXiv
A Critical Review of Machine Learning of Energy Materials
journal, January 2020
- Chen, Chi; Zuo, Yunxing; Ye, Weike
- Advanced Energy Materials, Vol. 10, Issue 8
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery
text, January 2019
- Kailkhura, Bhavya; Gallagher, Brian; Kim, Sookyung
- Unpublished
Deep Neural Networks for Accurate Predictions of Garnet Stability
text, January 2017
- Ye, Weike; Chen, Chi; Wang, Zhenbin
- arXiv
Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition
journal, November 2019
- Sutton, Christopher; Ghiringhelli, Luca M.; Yamamoto, Takenori
- npj Computational Materials, Vol. 5, Issue 1
A general-purpose machine learning framework for predicting properties of inorganic materials
journal, August 2016
- Ward, Logan; Agrawal, Ankit; Choudhary, Alok
- npj Computational Materials, Vol. 2, Issue 1
Data‐Driven Materials Science: Status, Challenges, and Perspectives
journal, September 2019
- Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
- Advanced Science, Vol. 6, Issue 21
Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning
journal, January 2019
- Li, Zhenzhu; Xu, Qichen; Sun, Qingde
- Advanced Functional Materials, Vol. 29, Issue 9
Finding New Perovskite Halides via Machine Learning
journal, April 2016
- Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho
- Frontiers in Materials, Vol. 3
Towards Photoferroic Materials by Design: Recent Progresses and Perspective
text, January 2019
- Castelli, Ivano E.; Olsen, Thomas; Chen, Yunzhong
- arXiv
Identifying Pb-free perovskites for solar cells by machine learning
journal, March 2019
- Im, Jino; Lee, Seongwon; Ko, Tae-Wook
- npj Computational Materials, Vol. 5, Issue 1
Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery
journal, November 2019
- Nagarajan, Nagasundaram; Yapp, Edward K. Y.; Le, Nguyen Quoc Khanh
- BioMed Research International, Vol. 2019
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
text, January 2018
- Choudhary, Kamal; DeCost, Brian; Tavazza, Francesca
- arXiv
Assessment of the GLLB-SC potential for solid-state properties and attempts for improvement
text, January 2017
- Tran, Fabien; Ehsan, Sohaib; Blaha, Peter
- arXiv
Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data
journal, August 2019
- Suzuki, Keisuke; Toyao, Takashi; Maeno, Zen
- ChemCatChem, Vol. 11, Issue 18
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra
journal, January 2019
- Ghosh, Kunal; Stuke, Annika; Todorović, Milica
- Advanced Science, Vol. 6, Issue 9
Data‐Driven Materials Science: Status, Challenges, and Perspectives
journal, September 2019
- Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
- Advanced Science, Vol. 6, Issue 21
Solving the electronic structure problem with machine learning
journal, February 2019
- Chandrasekaran, Anand; Kamal, Deepak; Batra, Rohit
- npj Computational Materials, Vol. 5, Issue 1
Finding New Perovskite Halides via Machine Learning
journal, April 2016
- Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho
- Frontiers in Materials, Vol. 3
Investigation of structural, magneto-electronic, and thermoelectric response of ductile SnAlO 3 from high-throughput DFT calculations: KHANDY and GUPTA
journal, February 2017
- Khandy, Shakeel Ahmad; Gupta, Dinesh C.
- International Journal of Quantum Chemistry, Vol. 117, Issue 8
Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery
journal, November 2019
- Nagarajan, Nagasundaram; Yapp, Edward K. Y.; Le, Nguyen Quoc Khanh
- BioMed Research International, Vol. 2019
A strategy to apply machine learning to small datasets in materials science
journal, May 2018
- Zhang, Ying; Ling, Chen
- npj Computational Materials, Vol. 4, Issue 1
Deep neural networks for accurate predictions of crystal stability
journal, September 2018
- Ye, Weike; Chen, Chi; Wang, Zhenbin
- Nature Communications, Vol. 9, Issue 1