Material Screening for Solid State Electrolyte Applications through Machine Learning and Ab-initio Molecular Dynamic Calculations
Bibliographic record
Abstract
Solid state electrolyte batteries provide a noticeable advantage over conventional liquid batteries due to improved safety and their potential for increased energy storage over current batteries. For this thesis, two types of calculations are undertaken: first, a machine learning neural network has been used to predict optimal systems; second, first principle Ab-Initio Molecular Dynamic (AIMD) calculations have been performed to predict the performance of the systems previously identified. Machine learning was implemented using the Tensorflow library, where a variety of neural networks were optimized and tested in the categorization and discovery of promising solid state electrolytes. AIMD was implemented using the Vienna Ab-Initio Software Package (VASP), where promising materials systems were simulated and then had ionic conductivities determined from these simulation results. The AIMD portion of this study focused on the Argyrodite system class, which was found during literature review and through the use of the preceding machine learning calculations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".