Critical Role of Framework Flexibility and Disorder in Driving High Ionic Conductivity in LiNbOCl<sub>4</sub>
Bibliographic record
Abstract
Understanding Li-ion transport is key for the rational design of superionic solid electrolytes with exceptional ionic conductivities. LiNbOCl 4 is reported to be one of the most highly conducting materials in the recently realized new class of soft oxyhalide solid electrolytes, exhibiting an ionic conductivity of ∼11 mS·cm –1 . Here, we apply X-ray/neutron diffraction and pair distribution function analysis─coupled with density functional theory/ab initio molecular dynamics (AIMD)─to determine a structural model that provides a rationale for the high conductivity that we observe experimentally in this nanocrystalline solid. We show that it arises from unusually high framework flexibility at room temperature. This is due to isolated 1-D [NbOCl 4 ] − anionic chains that exhibit energetically favorable orientational disorder that is─in turn─correlated to multiple, disordered, and equi-energetic Li + sites in the lattice. As the Li ions sample the 3-D energy landscape with a fast predicted diffusion coefficient of 5.1 × 10 –7 cm 2 /s at room temperature (σ i calc = 17.4 mS·cm –1 ), the inorganic polymer chains can reorient or vice versa. The activation energy barrier for Li migration through the frustrated energy landscape is especially reduced by the elastic nature of the NbO 2 Cl 4 octahedra evident from very widely dispersed Cl–Nb–Cl bond angles in AIMD simulations at 300 K. The phonon spectra are predominantly influenced by Cl vibrations in the low energy range, and there is a strong overlap between the framework (Cl, Nb) and Li partial density of states in the region between 1.2 and 4.0 THz. The framework flexibility is also reflected in a relatively low bulk modulus of 22.7 GPa. Our findings pave the way for the investigation of future “flex-ion” inorganic solids and open up a new direction for the design of high-conductivity, soft solid electrolytes for all-solid-state batteries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".