(Invited) High-Throughput Development of LiCoPO<sub>4</sub>
Bibliographic record
Abstract
High voltage cathodes are attractive for high energy density Li-ion batteries, particularly with the innovation of solid electrolytes with large stability windows. However, candidates such as LiCoPO4 have presented numerous problems due to poor electronic/ionic conductivities. Typical solutions involving nanosizing result in extremely poor cycling performance. Herein, we first apply high-throughput methods to develop near-micron sized carbon-coated LiCoPO4 with improved energy density and capacity retention.1 In total, 1300 materials with 46 different substituents were synthesized and characterized. A number of substituents showed greatly improved capacity (e.g. 160 mAh/g for 1% In substitution vs. 95 mAh/g for the pristine). However, co-doping was required to improve extended cycling. Li1-3xCo1-2xInxMoxPO4 was found to be particularly effective with dramatically improved cycling (as high as 100 % after 10 cycles, vs. ~50 % in unsubstituted). While In improved the electronic conductivity of the carbon-coated materials, Mo co-doping gave larger particles and DFT calculations showed that Mo impedes the formation of Li/Co antisite defects. While the above described experiments were solely guided by the researchers intuition, we also explore potential benefits of using machine-learning (ML) algorithms to guide the composition exploration. Random compositions were first prepared to supplement our existing data as part of the training set, then further synthesis was performed based on the ML outputs. The results will be presented in detail to help establish to what extent ML can be used to further leverage our group's high-throughput data. References 1. A. Jonderian, S. Jia, G. Yoon, V.T. Cozea, N. Zeinali Galabi, S.B. Ma, and E. McCalla. Accelerated Development of High Voltage Li-Ion Cathodes. Adv. Energy Mater. 2022, 12, 2201704.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".