Atomic Cartography of High‐Entropy Cs <sub>2</sub> <i>B</i> Cl <sub>6</sub> Perovskite‐Inspired Materials: The Vital Role of Solid‐State NMR Spectroscopy in Identifying Elemental Disorder
Bibliographic record
Abstract
Abstract The burgeoning field of high‐entropy materials (HEMs) has sparked significant interest by leveraging synergistic “cocktail effects” from inexpensive and abundant elements to access unprecedented physical, optical, and chemical properties. While standard characterization techniques, such as diffraction and energy‐dispersive X‐ray spectroscopy, provide valuable insights, they often fall short in elucidating the intricate atomic‐level disorder and the presence of nanoscale phase separation or persistent nanodomains. To overcome these limitations, this work introduces a robust and rapid analytical method based on solid‐state 133 Cs nuclear magnetic resonance (NMR) spectroscopy, capable of directly probing atomic‐level mixing in these complex materials. This technique is demonstrated by exploring a series of Cs 2 B Cl 6 (where B represents various combinations of 1 to 8 elements at the B ‐site) perovskite‐inspired materials synthesized via multiple routes. Although X‐ray diffraction and EDX suggest successful HEM formation across all methods, 133 Cs NMR analysis reveals the prevalence of phase separation and preferred elemental clustering. A high‐energy mechanochemical synthetic approach is proven to drive atomic‐level mixing of up to eight elements. These results demonstrate the need for a synergistic approach that combines local atomic sensitivity using NMR methods with long‐range order diffraction methods to solve chemical structure and comprehensively assess rational design strategies for halogen‐containing materials.
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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 | 0.001 | 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) | 0.003 | 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".