Automatically Capturing Key Features for Predicting Superionic Conductivity of Solid-State Electrolytes Using a Neural Network
Bibliographic record
Abstract
Solid-state batteries (SSBs) are one of the most promising energy storage technologies due to their low flammability and high energy density compared with currently used liquid-state batteries. The main obstacle to SSB development, however, is the large chemical design space for the solid-state electrolytes (SSEs), as it is significantly time-consuming to screen candidates experimentally or from first-principles simulations. Toward this end, machine learning (ML) offers an efficient strategy. However, current ML models use complex manually created features as inputs based on human intuition, which can introduce human bias, are potentially difficult to obtain for many materials, and can result in a cumbersome feature selection process. This work demonstrates that a neural network-based model utilizing only two simple elemental features (group and period) and one simple structural feature (coordination number) can provide excellent predictive performance comparable to previous manual feature-based studies, while automatically capturing any potential secondary features and reducing the need for human intervention in model training. Such a model is potentially more generalizable than manual feature-based models and can be even applied to other material property predictions, while greatly reducing complexity and training time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".