A high-throughput data analysis and materials discovery tool for strongly correlated materials. (December 2018)

• Record Type: Journal Article
• Authors: Hafiz, Hasnain; Khair, Adnan; Choi, Hongchul; Mueen, Abdullah; Bansil, Arun; Eidenbenz, Stephan; Wills, John; Zhu, Jian-Xin; Balatsky, Alexander; Ahmed, Towfiq
• Is Part Of: Npj computational materials. Volume 4:issue 1(2018)
• Page Start: 1
• Page End: 9
• Publication Date: 2018-12
• Subjects: Materials science -- Computer simulation -- Periodicals; Materials science -- Mathematical models -- Periodicals; Materials science -- Computer simulation; Electronic journals; Periodicals; 620.110285
• DOI: 10.1038/s41524-018-0120-9
• Languages: English
• ISSNs: 2057-3960

Abstract

Abstract Modeling off -electron systems is challenging due to the complex interplay of the effects of spin–orbit coupling, electron–electron interactions, and the hybridization of the localizedf -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 ourf -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 off -electron based materials. Materials databases: Checked and analyzed F-electron systems can possess interesting properties, and a database on these specific compounds could aid materials discovery. Here, Hasnain Hafiz at Northeastern University, and colleagues at Los Alamos National Lab, present thef -electron structure database. In contrast to other databases, computational data is generated with all electrons, resulting in a better description of these materials. Experimental information can sometimes miss essential data, but here an artificial neural network is used to correct this incompleteness, enabling correct determination (with 99.1% accuracy) of a crystal system. To verify the database, eight known double perovskites (AA′BB′CC′) were successfully found, and four unknown stable double perovskites were predicted. Moreover, electronic structure analysis tools in the database identifiedf -electron localization trends across the periodic table. This data-driven approach could drive the discovery of newf -electron materials, and lead to new applications.