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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".