A Perspective on the Origin of High‐Entropy Solid Electrolytes
Bibliographic record
Abstract
As the key material for the all-solid-state batteries (ASSBs), solid electrolytes (SEs) have attracted increasing attention. Recently, a novel design strategy-high-entropy (HE) approach is frequently reported to improve the ionic conductivity and electrochemical performance of SEs. However, the fundamental understandings on the HE working mechanism and applicability evaluation of HE concept are deficient, which would impede the sustainable development of a desirable strategy to enable high-performance SEs. In this contribution, the essence of HE-related approaches and their positive effects on SEs are evaluated. The reported HE strategy stems from complex compositional regulations. The derived structural stability and enhanced property are originally from the modulated system disorder and subtle local-structure evolutions, respectively. While HE ardently describes the increased entropy/disorder during the modification of prevailing SEs, rigorous experimental formulations, and direct correlations between the HE structures and desired properties are necessary to be established. This perspective would be a timely and critical overview for the HE approaches in the context of SEs, aiming to stimulate further discussion and exploration in this emerging research direction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".