First-principles calculation of parameters of electron paramagnetic resonance spectroscopy in solids
Bibliographic record
Abstract
The hyperfine A-tensor and Zeeman g-tensor parameterize the interaction of an 'effective' electron spin with the magnetic field due to the nuclear spin and the homogeneous external magnetic field, respectively. The A- and g-tensors are the quantities of primary interest in electron paramagnetic resonance (EPR) spectroscopy. In this paper, we review our work [E.S. Kadantsev, T. Ziegler, J. Phys. Chem. A 2008, 112, 4521; E. S. Kadantsev, T. Ziegler, J. Phys. Chem. A 2009, 113, 1327] on the calculation of these EPR parameters under periodic boundary conditions (PBC) from first-principles. Our methodology is based on the Kohn-Sham DFT (KS DFT), explicit usage of Bloch basis set made up of numerical and Slater-type atomic orbitals (NAOs/STOs), and is implemented in the 'full potential' program BAND. Our implementation does not rely on the frozen core approximation. The NAOs/STOs basis is well suited for the accurate representation of the electron density near the nuclei, a prerequisite for the calculation of highly accurate hyperfine parameters. In the case of g-tensor, our implementation is based on the method of Van Lenthe et al. [E. van Lenthe, P. E. S. Wormer, A. van der Avoird, J. Chem. Phys. 1997, 107, 2488] in which the spin-orbital coupling is taken into account variationally. We demonstrate the viability of our scheme by calculating EPR parameters of paramagnetic defects in solids. We consider the A-tensor of 'normal' and 'anomalous' muonium defect in IIIA-VA semiconductors as well as the S2 anion radical in KCl host crystal lattice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".