Chapter 2 Density functional theory applied to 4f and 5f elements and metallic compounds

Richter, Manuel

doi:10.1016/s1567-2719(01)13006-4

Cited by 21 publications

(27 citation statements)

References 643 publications

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“…Since Au 3 Co is a hypothetical compound, the lattice constant was determined www.pss-b.com [19] of the full potential local orbital band structure scheme (FPLO) [18]. As a matter of experience, orbital magnetism of Co compounds is better described if the LSDA exchange-correlation (XC) energy functional is complemented by the orbital polarization (OP) energy functional suggested by Eriksson et al [21,22]:…”

Section: Methodsmentioning

confidence: 99%

Spin and orbital magnetism of Au₃Co: Density functional calculations

Sargolzaei

Opahle

Richter

2005

Physica Status Solidi (b)

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PACS 71.15. Mb, 75.20.Hr The metastable L1 2 compound Au 3 Co has been considered as a model for high Co impurity concentration in gold. The spin and orbital moments of this compound have been calculated in the framework of local spin density approximation (LSDA) with and without orbital polarization corrections, using the relativistic version of the full-potential local orbital minimum basis code. We also calculated the magnetocrystalline anisotropy energy.

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Section: Methodsmentioning

confidence: 99%

Spin and orbital magnetism of Au₃Co: Density functional calculations

Sargolzaei

Opahle

Richter

2005

Physica Status Solidi (b)

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“…For example, while the CF potential for f electrons is fully described by only two independent parameters for the cubic point groups G = m3m, 432,43m, there are 26 independent parameters for the lowest symmetry groups 1 and1 [24,25]. These CF parameters are notoriously difficult to determine in first-principle calculations [26], and are therefore best regarded as phenomenological parameters that are found from comparison to experimental results. While most accurate values of CF parameters are obtained from analyzing inelastic neutron scattering results [27,28], much insight can already be gained by much more straightforward measurements of thermodynamic observables such as the (magnetic part of the) specific heat c M (T ) as a function of temperature T , the magnetic susceptibility χ a (T ) along direction a, and the magnetization µ a (B, T ) in a finite magnetic field B.…”

Section: Introductionmentioning

confidence: 99%

Learning crystal field parameters using convolutional neural networks

et al. 2021

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We present a deep machine learning algorithm to extract crystal field (CF) Stevens parameters from thermodynamic data of rare-earth magnetic materials. The algorithm employs a two-dimensional convolutional neural network (CNN) that is trained on magnetization, magnetic susceptibility and specific heat data that is calculated theoretically within the single-ion approximation and further processed using a standard wavelet transformation. We apply the method to crystal fields of cubic, hexagonal and tetragonal symmetry and for both integer and half-integer total angular momentum values JJ of the ground state multiplet. We evaluate its performance on both theoretically generated synthetic and previously published experimental data on CeAgSb_22, PrAgSb_22 and PrMg_22Cu_99, and find that it can reliably and accurately extract the CF parameters for all site symmetries and values of JJ considered. This demonstrates that CNNs provide an unbiased approach to extracting CF parameters that avoids tedious multi-parameter fitting procedures.

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“…Actually, YCo 5 has become a real workbench for the study of the 3d orbital polarization, 11,12 as well as for the ab initio calculations of MAE and orbital magnetic moment. [13][14][15][16][17][18][19][20] At the same time, taking advantage of the structural regularity of the R mþn T 5mþ3n B 2n series, the researches have extended over various stabilized stoichiometries, particularly with m ¼ 1 and n ¼ 1, 2, 3, and infinity. Thus, neutron diffraction experiments performed on R nþ1 Co 3nþ5 B 2n (n ¼ 1, 2, 3 and infinity) have brought evidence on (i) the preferential occupation character of the 3d metal sites; 21,22 (ii) the dependence of the local cobalt magnetic moments on the details of the local environment, particularly on the presence of B atoms nearest neighbors (n.n.…”

Section: Introductionmentioning

confidence: 99%

First principles calculations, neutron, and x-ray diffraction investigation of Y3Ni13B2, Y3Co13B2, and Y3Ni10Co3B2

Plugaru

Văleanu

Plugaru

et al. 2014

Journal of Applied Physics

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Fully relativistic calculations within the local spin density approximation and the generalized gradient approximation were performed to determine the local spin and orbital magnetic moments, as well as the magnetocrystalline anisotropy energy of Y 3 Ni 13 B 2 , Y 3 Co 13 B 2 , and Y 3 Ni 10 Co 3 B 2 compounds. A weak in-plane magnetic anisotropy is determined for Y 3 Ni 13 B 2 , under the assumption of a crystallographic-like magnetic unit cell and collinear magnetic moments. The calculations predict considerable c-axis anisotropy for Y 3 Co 13 B 2 and Y 3 Ni 10 Co 3 B 2 , but smaller than that of YCo 5 . The values of the magnetocrystalline anisotropy energy correlate well with both the magnitude of the orbital magnetic moment and the orbital magnetic moment anisotropy. The mixing between Co or Ni 3d states and B 2p states, observable at the bottom of the valence band of the 3d metal having a boron atom nearest neighbor, decreases the 3d spin and especially, the 3d orbital magnetic moments. Y 3 Ni 13 B 2 and Y 3 Ni 10 Co 3 B 2 were also investigated by powder neutron diffraction experiments, at temperatures between 1.8 and 249 K. The Co and Ni site averaged magnetic moments calculated in the mixed compound are in fair agreement with the values obtained by the refinement of the magnetic contribution to the diffraction pattern. V C 2014 AIP Publishing LLC.

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Chapter 2 Density functional theory applied to 4f and 5f elements and metallic compounds

Cited by 21 publications

References 643 publications

Spin and orbital magnetism of Au₃Co: Density functional calculations

Spin and orbital magnetism of Au₃Co: Density functional calculations

Learning crystal field parameters using convolutional neural networks

First principles calculations, neutron, and x-ray diffraction investigation of Y3Ni13B2, Y3Co13B2, and Y3Ni10Co3B2

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Chapter 2 Density functional theory applied to 4f and 5f elements and metallic compounds

Cited by 21 publications

References 643 publications

Spin and orbital magnetism of Au3Co: Density functional calculations

Spin and orbital magnetism of Au3Co: Density functional calculations

Learning crystal field parameters using convolutional neural networks

First principles calculations, neutron, and x-ray diffraction investigation of Y3Ni13B2, Y3Co13B2, and Y3Ni10Co3B2

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Spin and orbital magnetism of Au₃Co: Density functional calculations

Spin and orbital magnetism of Au₃Co: Density functional calculations