Polaron in TiO<sub>2</sub> from First‐Principles: A Review
Bibliographic record
Abstract
Abstract Often the choice of semiconductor material for light‐chemical energy conversion in solar cell technology is TiO 2 polymorphs. However, quantum‐mechanical phenomena of electron self‐trapping (polaron) in these polar semiconductors present a challenge to optimize their performance for absorption of solar radiation. The electron trapped in the defect site of the lattice may suppress the recombination of charge carriers and improve the efficiency of light‐chemical energy conversion. Therefore, it is crucial to study polarons in transition metal oxides and semiconductors using first‐principles methods to elucidate the nature of charge carriers and trapping sites for ameliorating the performance of energy conversion. In this work, a comprehensive review is presented using selected literature of polaron studies in TiO 2 from first‐principles methods. Overview of the Landau–Pekar model to the recent development of ab initio theory of polaron is presented. The popular DFT+U approach and hybrid functional method are discussed to show the general way of studying polaron using ab initio methods. Introduction of electron‐phonon interaction and the ab initio theory of polaron are briefly presented Therefore, this review presents the development of first‐principles methods from Landau theory to the state‐of‐the‐art to study polarons using TiO 2 as the toy model. Finally, conclusions and future perspectives are outlined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".