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Title: A high-throughput data analysis and materials discovery tool for strongly correlated materials

Abstract

Modeling of f-electron systems is challenging due to the complex interplay of the effects of spin–orbit coupling, electron–electron interactions, and the hybridization of the localized f-electrons with itinerant conduction electrons. This complexity drives not only the richness of electronic properties but also makes these materials suitable for diverse technological applications. In this context, we propose and implement a data-driven approach to aid the materials discovery process. By deploying state-of-the-art algorithms and query tools, we train our learning models using a large, simulated dataset based on existing actinide and lanthanide compounds. The machine-learned models so obtained can then be used to search for new classes of stable materials with desired electronic and physical properties. We discuss the basic structure of our f-electron database, and our approach towards cleaning and correcting the structure data files. Illustrative examples of the applications of our database include successful prediction of stable superstructures of double perovskites and identification of a number of physically-relevant trends in strongly correlated features of f-electron based materials.

Authors:
; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States); Northeastern Univ., Boston, MA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1619623
Alternate Identifier(s):
OSTI ID: 1543756; OSTI ID: 1557742
Report Number(s):
LA-UR-17-28925
Journal ID: ISSN 2057-3960; 63; PII: 120
Grant/Contract Number:  
AC02-05CH11231; 20170450ER (LANL-LDRD); FG02-07ER46352; 89233218CNA000001
Resource Type:
Published Article
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Name: npj Computational Materials Journal Volume: 4 Journal Issue: 1; Journal ID: ISSN 2057-3960
Publisher:
Nature Publishing Group
Country of Publication:
United Kingdom
Language:
English
Subject:
Chemistry; Materials Science; 36 MATERIALS SCIENCE

Citation Formats

Hafiz, Hasnain, Khair, Adnan Ibne, Choi, Hongchul, Mueen, Abdullah, Bansil, Arun, Eidenbenz, Stephan, Wills, John, Zhu, Jian-Xin, Balatsky, Alexander V., and Ahmed, Towfiq. A high-throughput data analysis and materials discovery tool for strongly correlated materials. United Kingdom: N. p., 2018. Web. doi:10.1038/s41524-018-0120-9.
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Hafiz, Hasnain, Khair, Adnan Ibne, Choi, Hongchul, Mueen, Abdullah, Bansil, Arun, Eidenbenz, Stephan, Wills, John, Zhu, Jian-Xin, Balatsky, Alexander V., & Ahmed, Towfiq. A high-throughput data analysis and materials discovery tool for strongly correlated materials. United Kingdom. https://doi.org/10.1038/s41524-018-0120-9
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Hafiz, Hasnain, Khair, Adnan Ibne, Choi, Hongchul, Mueen, Abdullah, Bansil, Arun, Eidenbenz, Stephan, Wills, John, Zhu, Jian-Xin, Balatsky, Alexander V., and Ahmed, Towfiq. Thu . "A high-throughput data analysis and materials discovery tool for strongly correlated materials". United Kingdom. https://doi.org/10.1038/s41524-018-0120-9.
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@article{osti_1619623,
title = {A high-throughput data analysis and materials discovery tool for strongly correlated materials},
author = {Hafiz, Hasnain and Khair, Adnan Ibne and Choi, Hongchul and Mueen, Abdullah and Bansil, Arun and Eidenbenz, Stephan and Wills, John and Zhu, Jian-Xin and Balatsky, Alexander V. and Ahmed, Towfiq},
abstractNote = {Modeling of f-electron systems is challenging due to the complex interplay of the effects of spin–orbit coupling, electron–electron interactions, and the hybridization of the localized f-electrons with itinerant conduction electrons. This complexity drives not only the richness of electronic properties but also makes these materials suitable for diverse technological applications. In this context, we propose and implement a data-driven approach to aid the materials discovery process. By deploying state-of-the-art algorithms and query tools, we train our learning models using a large, simulated dataset based on existing actinide and lanthanide compounds. The machine-learned models so obtained can then be used to search for new classes of stable materials with desired electronic and physical properties. We discuss the basic structure of our f-electron database, and our approach towards cleaning and correcting the structure data files. Illustrative examples of the applications of our database include successful prediction of stable superstructures of double perovskites and identification of a number of physically-relevant trends in strongly correlated features of f-electron based materials.},
doi = {10.1038/s41524-018-0120-9},
journal = {npj Computational Materials},
number = 1,
volume = 4,
place = {United Kingdom},
year = {Thu Nov 22 00:00:00 EST 2018},
month = {Thu Nov 22 00:00:00 EST 2018}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1038/s41524-018-0120-9
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Citation Metrics:
Cited by: 11 works
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Figures / Tables:

Fig. 1 Fig. 1: Confusion matrices for different machine-learning algorithms. Confusion matrices for three methods: a Multilayer Perceptron (MLP), b K-Nearest-Neighbor (KNN), and c Logistic Regression (LR). MLP, which is a neural network approach, captures the correct crystal system with maximum accuracy
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