Machine learning bandgaps of double perovskites

Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; Ramprasad, R.; Gubernatis, J. E.; Lookman, T.

doi:10.1038/srep19375

Title: Machine learning bandgaps of double perovskites

Journal Article · Tue Jan 19 00:00:00 EST 2016 · Scientific Reports

DOI:https://doi.org/10.1038/srep19375· OSTI ID:1235935

Pilania, G. ^[1]; Mannodi-Kanakkithodi, A. ^[2]; Uberuaga, B. P. ^[1]; Ramprasad, R. ^[2]; Gubernatis, J. E. ^[1]; Lookman, T. ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Univ. of Connecticut, Storrs, CT (United States)

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1235935

Report Number(s):: LA-UR-15-23084; srep19375

Journal Information:: Scientific Reports, Vol. 6; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 274 works

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A hybrid organic-inorganic perovskite dataset Kim, Chiho; Huan, Tran Doan; Krishnan, Sridevi Scientific Data, Vol. 4, Issue 1 https://doi.org/10.1038/sdata.2017.57	journal	May 2017
Graph Neural Network for Hamiltonian-Based Material Property Prediction Bai, Hexin; Chu, Peng; Tsai, Jeng-Yuan arXiv https://doi.org/10.48550/arxiv.2005.13352	preprint	January 2020
A Critical Review of Machine Learning of Energy Materials Chen, Chi; Zuo, Yunxing; Ye, Weike Advanced Energy Materials, Vol. 10, Issue 8 https://doi.org/10.1002/aenm.201903242	journal	January 2020
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery Kailkhura, Bhavya; Gallagher, Brian; Kim, Sookyung Unpublished https://doi.org/10.13140/rg.2.2.15103.25766	text	January 2019
Deep Neural Networks for Accurate Predictions of Garnet Stability Ye, Weike; Chen, Chi; Wang, Zhenbin arXiv https://doi.org/10.48550/arxiv.1712.01908	text	January 2017
Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition Sutton, Christopher; Ghiringhelli, Luca M.; Yamamoto, Takenori npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0239-3	journal	November 2019
A general-purpose machine learning framework for predicting properties of inorganic materials Ward, Logan; Agrawal, Ankit; Choudhary, Alok npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2016.28	journal	August 2016
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Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning Li, Zhenzhu; Xu, Qichen; Sun, Qingde Advanced Functional Materials, Vol. 29, Issue 9 https://doi.org/10.1002/adfm.201807280	journal	January 2019
Finding New Perovskite Halides via Machine Learning Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho Frontiers in Materials, Vol. 3 https://doi.org/10.3389/fmats.2016.00019	journal	April 2016
Towards Photoferroic Materials by Design: Recent Progresses and Perspective Castelli, Ivano E.; Olsen, Thomas; Chen, Yunzhong arXiv https://doi.org/10.48550/arxiv.1906.04490	text	January 2019
Identifying Pb-free perovskites for solar cells by machine learning Im, Jino; Lee, Seongwon; Ko, Tae-Wook npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0177-0	journal	March 2019
Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery Nagarajan, Nagasundaram; Yapp, Edward K. Y.; Le, Nguyen Quoc Khanh BioMed Research International, Vol. 2019 https://doi.org/10.1155/2019/8427042	journal	November 2019
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape Choudhary, Kamal; DeCost, Brian; Tavazza, Francesca arXiv https://doi.org/10.48550/arxiv.1805.07325	text	January 2018
Assessment of the GLLB-SC potential for solid-state properties and attempts for improvement Tran, Fabien; Ehsan, Sohaib; Blaha, Peter arXiv https://doi.org/10.48550/arxiv.1712.06508	text	January 2017
Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data Suzuki, Keisuke; Toyao, Takashi; Maeno, Zen ChemCatChem, Vol. 11, Issue 18 https://doi.org/10.1002/cctc.201900971	journal	August 2019
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra Ghosh, Kunal; Stuke, Annika; Todorović, Milica Advanced Science, Vol. 6, Issue 9 https://doi.org/10.1002/advs.201801367	journal	January 2019
Solving the electronic structure problem with machine learning Chandrasekaran, Anand; Kamal, Deepak; Batra, Rohit npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0162-7	journal	February 2019
Investigation of structural, magneto-electronic, and thermoelectric response of ductile SnAlO ₃ from high-throughput DFT calculations: KHANDY and GUPTA Khandy, Shakeel Ahmad; Gupta, Dinesh C. International Journal of Quantum Chemistry, Vol. 117, Issue 8 https://doi.org/10.1002/qua.25351	journal	February 2017
A strategy to apply machine learning to small datasets in materials science Zhang, Ying; Ling, Chen npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0081-z	journal	May 2018
Deep neural networks for accurate predictions of crystal stability Ye, Weike; Chen, Chi; Wang, Zhenbin Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-06322-x	journal	September 2018

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