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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 Laboratory (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:
1557742
Alternate Identifier(s):
OSTI ID: 1543756
Report Number(s):
LA-UR-17-28925
Journal ID: ISSN 2057-3960; PII: 120
Grant/Contract Number:  
AC02-05CH11231; FG02-07ER46352; 89233218CNA000001
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2057-3960
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
Chemistry; 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 States: N. p., 2018. Web. doi:10.1038/s41524-018-0120-9.
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 States. doi:10.1038/s41524-018-0120-9.
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 States. doi:10.1038/s41524-018-0120-9. https://www.osti.gov/servlets/purl/1557742.
@article{osti_1557742,
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},
issn = {2057-3960},
number = 1,
volume = 4,
place = {United States},
year = {2018},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

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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