Materials Graph Networks Assisting the Discovery of New Solid-State Electrolyte Materials
Bibliographic record
Abstract
Finding novel solid-state electrolyte materials with specific desired properties is one of the main challenges of first principles-based modelling due to its high computational cost. Recently, machine learning (ML) has been used exclusively in the materials discovery field due to its remarkable capabilities to process large amounts of data and extract useful information insights. The implementation of ML into atomic-scale materials modeling can accelerated materials sampling with first principles accuracy, this should provide a short workflow and highly reduce the computational cost. In this work, we facilitate the ML model that can effectively screen for targeted properties from the entire chemical database possibility, including those materials that have never been examined. Currently, we are developing a materials graph networks framework for representing periodic crystal systems with the capability to learn atomistic chemical insights, especially for the discovery of new solid-state electrolyte materials. In this talk, we will discuss the basic theory and our recent work towards the development of ML model that can extensively discover and explore materials. We will highlight the computational-guided evolution approaches and screening high-performance Li conductor materials. Finally, we will end on discussing the future of ML-assisted materials discovery to the future of all-solid-state lithium batteries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".