Improved description of perovskite oxide crystal structure and electronic properties using self-consistent Hubbard <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>U</mml:mi></mml:math> corrections from ACBN0
Bibliographic record
Abstract
The wide variety of complex physical behavior exhibited in transition metal oxides, particularly the perovskites $AB{\mathrm{O}}_{3}$, makes them a material family of interest in many research areas, but the drastically different electronic structures possible in these oxides raises challenges in describing them accurately within density functional theory (DFT) and related methods. Here, we evaluate the ability of the ACBN0, a recently developed first-principles approach to computing the Hubbard $U$ correction self-consistently, to describe the structural and electronic properties of the first-row transition metal perovskites with $\left(B=\mathrm{V}\ensuremath{-}\mathrm{Ni}\right)$. ACBN0 performs competitively with hybrid functional approaches such as the Heyd-Scuseria-Ernzerhof functional even when they are optimized empirically, at a fraction of the computational cost. ACBN0 also describes both the structure and band gap of the oxides more accurately than a conventional Hubbard $U$ correction performed by using $U$ values taken from the literature.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".