Transferable Local Pseudopotentials Derived via Inversion of the Kohn-Sham Equations in a Bulk Environment
Bibliographic record
Abstract
further decomposed to obtain an atom-centered LPS. We show that LPS’s derived from bulk environments are substantially more transferable than those derived from atoms alone. In KS-DFT tests on Si, we show that this bulk-derived LPS can reproduce accurately phase orderings predicted by nonlocal pseudopotentials for both metallic and semiconducting phases. We then tested this LPS in OF-DFT calculations on Si crystals, where we demonstrate that this bulk-derived LPS ~BLPS!, combined with a linear-response-based kinetic energy density functional with a density-dependent kernel, correctly predicts a diamond structure ground state for Si in an OF-DFT calculation. Other bulk properties, such as defect formation energies and transition pressures are also presented as tests of this BLPS. This approach for deriving LPS’s isolates much of the remaining error in OF-DFT to the kinetic energy density functional, providing means to test new functionals as they become available.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".