@article{c3cdb75f975b43c785d75cb82a0fdd99,
title = "Benchmark for Ab Initio Prediction of Magnetic Structures Based on Cluster-Multipole Theory BENCHMARK for AB INITIO PREDICTION of ... HUEBSCH et al.",
abstract = "The cluster-multipole (CMP) expansion for magnetic structures provides a scheme to systematically generate candidate magnetic structures specifically including noncollinear magnetic configurations adapted to the crystal symmetry of a given material. A comparison with the experimental data collected on MAGNDATA shows that the most stable magnetic configurations in nature are linear combinations of only few CMPs. Furthermore, a high-throughput calculation for all candidate magnetic structures is performed in the framework of spin-density functional theory (SDFT). We benchmark the predictive power of CMP+SDFT with 2935 calculations, which show that (i) the CMP expansion administers an exhaustive list of candidate magnetic structures, (ii) CMP+SDFT can narrow down the possible magnetic configurations to a handful of computed configurations, and (iii) SDFT reproduces the experimental magnetic configurations with an accuracy of ±0.5μB. For a subset the impact of on-site Coulomb repulsion U is investigated by means of 1545 CMP+SDFT+U calculations revealing no further improvement on the predictive power. ",
author = "Huebsch, {M. T.} and T. Nomoto and Suzuki, {M. T.} and R. Arita",
note = "Funding Information: We are thankful for useful discussions with Stephan Huebsch, Takashi Koretsune, and Saeed Bahramy. Moreover, we gratefully acknowledge the Center for Computational Materials Science, Institute for Materials Research, Tohoku University for the use of MASAMUNE-IMR (MAterials science Supercomputing system for Advanced MUlti-scale simulations towards NExt-generation–Institute for Materials Research) (Project No. 19S0005). This work was supported by a Grant-in-Aid for Scientific Research (No. 19H05825 and No. 16H06345) from the Ministry of Education, Culture, Sports, Science and Technology, and CREST (JPMJCR18T3) from the Japan Science and Technology Agency, and JSPS KAKENHI Grants No. JP15H05883 (J-Physics), No. JP19H01842, No. JP20H05262, and No. JP20K21067, and JST PRESTO Grants No. JPMJPR17N8 and No. JPMJPR20L7. Funding Information: We are thankful for useful discussions with Stephan Huebsch, Takashi Koretsune, and Saeed Bahramy. Moreover, we gratefully acknowledge the Center for Computational Materials Science, Institute for Materials Research, Tohoku University for the use of MASAMUNE-IMR (MAterials science Supercomputing system for Advanced MUlti-scale simulations towards NExt-generation-Institute for Materials Research) (Project No. 19S0005). This work was supported by a Grant-in-Aid for Scientific Research (No. 19H05825 and No. 16H06345) from the Ministry of Education, Culture, Sports, Science and Technology, and CREST (JPMJCR18T3) from the Japan Science and Technology Agency, and JSPS KAKENHI Grants No. JP15H05883 (J-Physics), No. JP19H01842, No. JP20H05262, and No. JP20K21067, and JST PRESTO Grants No. JPMJPR17N8 and No. JPMJPR20L7. Publisher Copyright: {\textcopyright} 2021 authors. Published by the American Physical Society.",
year = "2021",
month = feb,
day = "16",
doi = "10.1103/PhysRevX.11.011031",
language = "English",
volume = "11",
journal = "Physical Review X",
issn = "2160-3308",
publisher = "American Physical Society",
number = "1",
}