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

Hafiz, Hasnain; Khair, Adnan Ibne; Choi, Hongchul; Mueen, Abdullah; Bansil, Arun; Eidenbenz, Stephan; Wills, John; Zhu, Jian-Xin; Balatsky, Alexander V.; Ahmed, Towfiq

doi:10.1038/s41524-018-0120-9

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

Journal Article · Thu Nov 22 00:00:00 EST 2018 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-018-0120-9· OSTI ID:1619623

Hafiz, Hasnain; Khair, Adnan Ibne; Choi, Hongchul; Mueen, Abdullah; Bansil, Arun; Eidenbenz, Stephan; Wills, John; Zhu, Jian-Xin; Balatsky, Alexander V.; Ahmed, Towfiq

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.

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Research Organization:: 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 Organization:: USDOE Office of Science (SC); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC02-05CH11231; 20170450ER (LANL-LDRD); FG02-07ER46352; 89233218CNA000001

OSTI ID:: 1619623

Alternate ID(s):: OSTI ID: 1543756; OSTI ID: 1557742

Report Number(s):: LA-UR-17-28925; 63; PII: 120

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Vol. 4 Journal Issue: 1; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Cited by: 11 works

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