Exploring Charged Defects and Dopability Limits of Solid Electrolytes, a Computational Study
Bibliographic record
Abstract
All-Solid-State Lithium Batteries (ASSLBs) are a new generation of lithium batteries that is developed to meet high expectations in terms of safety, stability and high energy density. The liquid electrolyte of conventional Li-ion batteries is replaced in ASSLBs by a safer and more stable solid electrolyte (SE). These solid electrolytes must meet a number of requirements before they can be considered in ASSLBs, including a wide electrochemical stability window, a high ionic conductivity and a negligible electronic conductivity [1] . For a long time, researchers have focused on achieving the highest ionic conductivity possible on these materials, comparable to the one of liquid electrolytes. The ionic conductivity in SEs has been successfully increased by the introduction of defects (doping) and Li1.3Al0.3Ti1.7(PO4)3 and Li1.5Al0.5Ge1.5(PO4)3 solid electrolytes are both excellent examples of such achievement. However, the formation of defects can also have a significant effect on the SEs electronic conductivity [2] , [ 3] , [ 4] . Increasing the electronic conductivity in solid electrolytes is detrimental to the ASSLBs safety and integrity. Therefore, it is essential to understand the defect chemistry in SEs. Due to their negligible concentration, characterizing point defects is hardly possible using standard characterization techniques, justifying the need for first-principles calculations. In this work, we have investigated the defect chemistry of most common solid electrolytes (LixM2(PO4)3 (M = Zr, Ti, Ge, Al), Li7La3Zr2O12, LiLaTi2O6, Li10Ge(PS6)2, Li7P3S11, Li3PS4, Li3PO4 and LiPO3). For each SE, we computed the formation energies for intrinsic defects and assessed the dopability limits as a function of the synthesis conditions. We found that the position of the Fermi level and dopability limits depend strongly on lithium and oxygen/sulfur chemical potentials but also on the nature of the solid electrolyte. We advocate that it is imperative to better regulate the synthesis conditions if we want to gain control over the formation of defects in solid electrolytes. The outcoming properties of these materials, such as the ionic and electronic conductivities, depend on it. [1] N. Dudney. Springer. 2003, 624-642. [2] E. Zhao et al. J. Alloys Comp. 2019. 782, 384-391. [3] Y. Song et al. J. Mat. Chem. A. 2019, 7(40), 22898-22902. [4] Y. Shan et al. J. Power Sources. 1995, 54(2), 397-402.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".