<i>Ab initio</i> modeling and design of vanadia-based electrode materials for post-lithium batteries
Bibliographic record
Abstract
Abstract In the efforts to develop novel active electrode materials for metal ion batteries, vanadium oxides are unique in that they have shown practically relevant performance characteristics for storage of several types of cations (such as Li, Na, Mg, K, and Al). Multiple experimentally accessible stoichiometries and phases, coupled with possibilities of modifications, create abundant design opportunities but require rational rather than ad hoc approaches. Because the mechanism of charge-discharge involves changes in occupancy and energies of electronic states, ab initio modeling is indispensable to understand limits of performance to which experiment can strive and to guide the design. We first summarize the theoretical basics of transition metal oxide chemistry and the fundamentals of first-principles investigations of battery materials and then review existing ab initio literature on vanadium oxides as active electrode materials for different classes of metal-ion batteries. We highlight the extent to which such modeling is able to describe or guide experiment and highlight the need for truly comparative computational studies among multiple phases and different active cations. Specifically, we discuss the computation of voltage-capacity curves as well as approaches to model cation diffusion kinetics in vanadia hosts from first principles. We describe how ab initio modeling can help design improved vanadia-based electrode materials by using doping and amorphization. We also highlight methodological and computational issues associated with modeling of vanadia or cation-intercalated vanadia, including the modeling of phase ordering, treatment of dispersion interactions, and application of the Hubbard correction especially in the context of comparing computed electrochemical performance of layered and non-layer phases and metallic and semiconductor phases. We also point out the inadequacy of the commonly used concept of formal oxidation states to understand the mechanism of charge-discharge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".