Mixed-Halide Perovskite Alloys CsPb(I<sub>1–<i>x</i></sub>Br<sub><i>x</i></sub>)<sub>3</sub> and CsPb(Br<sub>1–<i>x</i></sub>Cl<sub><i>x</i></sub>)<sub>3</sub>: New Insight of Configurational Entropy Effect from First-Principles and Phase Diagrams
Bibliographic record
Abstract
Stability is one of the key issues in mixed-halide perovskite alloys that are promising in emergent optoelectronics. Previous density functional theory (DFT) and machine learning studies indicate that the formation-energy convex hulls of these materials are very shallow, and stable alloy compositions are rare. In this work, we revisit this problem using DFT, with a special focus on the effects of configurational and vibrational entropies. Allowed by the 20-atomic models for the C s P b ( I 1 − x B r x ) 3 and C s P b ( B r 1 − x C l x ) 3 series, the partition functions and therewith thermodynamic state functions are calculated by traversing all possible mixed-halide configurations. We can thus evaluate the temperature- and system-dependent configurational entropy, which largely corrects the conventional approach based on the ideal solution model. Finally, temperature–composition phase diagrams that include α, β, γ, and δ phases of both alloys are constructed based on the free energy data, for which the contribution of phonon vibrations is included.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".