Cation-Ordering-Driven Design of Superionic Lithium Halospinels
Bibliographic record
Abstract
Reducing the material cost of inorganic solid-state electrolytes is crucial to advancing all-solid-state batteries (ASSBs) for next-generation energy storage applications. The halospinel Li 2 Sc 2/3 Cl 4 solid electrolyte (SE) possesses a high ionic conductivity of 1.5 mS cm –1 and good cycling stability up to 4.6 V. However, the high cost of Sc limits its practical application. In this study, we combine M3GNET universal machine learning interatomic potential (UMLIP) and density functional theory (DFT) for efficient screening of lower-cost cation-substituted halospinel compositions for synthesis. As a cost-mitigation strategy, predicted Mg 2+ -, Al 3+ -, and Zr 4+ -substituted Li 2 Sc 2/3 Cl 4 spinels with substitution fractions ranging from 20.9% to 37.5% were experimentally synthesized with only minor impurities, achieving room-temperature ionic conductivities as high as 1.85 mS cm –1 . Substitution of Fe 3+ was also achieved, albeit with a 7% Fe 2+ impurity. Molecular dynamics simulations (MD) using highly accurate moment tensor potentials (MTPs) indicate that Li + /Sc 3+ /M n+ ordering plays a crucial role in determining the conductivity of disordered substituted compositions. ASSBs operating at 3.8 mAh cm –2 capacity with Li 1.75 Sc 0.416 Zr 0.25 Cl 4 at a high current density of 2 mA cm –2 exhibited 80% of the capacity of more moderately loaded ASSBs cycled at a low rate. This work provides a foundational methodology for predicting the thermodynamic stability and ion transport of disordered lithium solid electrolytes and accelerating the discovery of novel materials for a range of applications.
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 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 | 0.000 |
| 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".