Machine Learning‐Driven Mass Discovery and High‐Throughput Screening of Fluoroether‐Based Electrolytes for High‐Stability Lithium Metal Batteries
Bibliographic record
Abstract
Abstract Developing novel fluoroether electrolytes with high‐voltage stability is an effective strategy to improve the performance of lithium metal batteries (LMB). However, the vast chemical space of fluoroether is underexplored due to the absence of effective tools to evaluate the potential used in high‐voltage LMB. Herein, a framework was developed in combination of Voting ensemble algorithms and graph convolution neural network (GCNN), allowing the fast assessment of oxidative stability of non‐aqueous liquid electrolytes, synthesizability of solvents as well as the solvation ability of them to dissolve lithium salts. Potential fluoroether solvent candidates for high‐voltage LMB were screened out from a virtual library comprising 5576 electrolytes constructed by a combination of 1510 solvents and 4 salts. Among them, two fluorinated ethers, 1,1,1,3,3,3‐hexafluoro‐2‐(2‐methoxyethoxy) propane and 7,7,8,8‐tetrafluoro‐3,12‐dimethoxy‐2,5,10,13‐tetraoxatetradecane, were successfully synthesized and showed satisfactory high‐voltage stability, sufficient solvation ability and satisfactory cycling with almost 99.5 % coulombic efficiency in Li||NMC811 full cell. This work provided an efficient framework for the discovery of solvents with high‐voltage tolerance in a vast structural space prior to experimental synthesis, accelerating the development of advanced electrolyte for high‐energy‐density rechargeable 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".