<i>(Invited)</i> Computational Insights into Polycrystalline Materials Used for Fuel Cells and Batteries
Bibliographic record
Abstract
The ability to manipulate and harness inherent disorder in materials -whether due to finite-dimensional defects like vacancies, dopants, and grain boundaries, or complete atomic randomness as seen in amorphous materials - can drive innovation in the design of energy materials. With the advancement of computational material science, we can now unravel the complexities introduced by these defects and identify pathways to optimize material efficiency. This talk will critically explore recent computational advancements in understanding the intricate behaviors of complex polycrystalline materials used in fuel cells and batteries, with a focus on optimizing them for next-generation devices. Materials used in solid oxide fuel cells (SOFCs) and lithium-ion batteries are typically polycrystalline, exhibiting variations in grain sizes, orientations, dopant segregation, and defect distribution, often with a high density of grain boundaries and interfaces that significantly impact their performance, particularly in terms of ionic conductivity. I will present our latest findings concerning the microstructural and ionic behaviors observed at grain boundaries and interfaces within prominent materials such as Yttria Stabilized Zirconia (YSZ) used in SOFCs, and Li-La-Ti-O (LLTO) employed in batteries. Through integrating classical and quantum simulations, our work has developed realistic models of these materials, providing explanations for observed experimental phenomena. Finally, I will discuss some of the challenges in identifying the overall impact of all the microstructural defects present in polycrystalline materials and the role that advanced computational modelling tools can play in resolving them. Acknowledgments The author acknowledges the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant program, Canada Research Chair (CRC) program, the Canada Foundation for Innovation (CFI) for infrastructure and operating funds. Computations were performed on the HPC supercomputer at the Calcul Québec and Compute Canada.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.018 |
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".