Beyond Order: Partial Site Occupancies-Informed Machine Learning for Solid-State Electrolytes Design
Bibliographic record
Abstract
Crystal structures with partial site occupancies (PSO), a common feature in disordered materials where ions fractionally occupy lattice sites, are crucial for designing solid-state electrolytes (SSEs) with a low activation energy ( E a ). However, deciphering the underlying structure–property relationships remains challenging, as traditional ordered-assumption models cannot fully explain PSO effects. Herein, we propose a PSO-informed machine learning (PSO-ML) method for SSEs design, in which PSO knowledge is leveraged by identifying crystal structures from the literature based on identical ionic species and Wyckoff sites criteria, enabling a quantitative correlation between PSO effects and E a . Applied to trigonal halides Li 3 YCl 6, an E a prediction model with a determination coefficient ( R 2 ) of 93.18% is obtained by partial least-squares analysis with leave-one-out cross-validation. The variable importance in projection analysis identifies the configurational entropy and interlayer distance as key descriptors, both dominated by Y 3+ occupancy effects. By optimizing Y 3+ occupancy, promising candidates Li 3 Y 4 x –3 M 3–3 x Cl 6 ( M: tetravalent cations, 0.75 < x ≤ 0.888) are suggested, where Li 3 Y 0.2 Zr 0.6 Cl 6 (room-temperature ionic conductivity of 1.19 mS cm –1 ) has been experimentally evaluated as an excellent candidate, and other promising compositions are waiting for validation. With high accuracy and interpretability, the PSO-ML method enables the accelerated discovery and design of SSEs and broader disordered materials.
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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 | 0.000 |
| 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".