Leveraging Computational Advances to Design and Optimize Materials for CO<sub>2</sub> Capture and Conversion
Bibliographic record
Abstract
The functionality of the materials used for energy applications is critically determined by the physical properties of small active regions such as dopants, dislocations, interfaces, grain boundaries, etc. The capability to manipulate and utilize the inevitable disorder in materials, whether due to the finite-dimensional defects (such as vacancies, dopants, grain boundaries) or due to the complete atomic randomness (as in amorphous materials), can bring innovation in designing energy materials. With the increase in computational material science capabilities, it is now possible to understand the complexity present in materials due to various defects resulting in pathways required for optimizing their efficiencies. In this talk, I will provide a critical overview of such computational advancements specifically for designing materials for sustainable CO2 capture and conversion technologies. I'll provide a comprehensive review of our recent research efforts, which involve employing traditional methods like density functional theory (DFT), alongside leveraging the data they generate to implement machine learning techniques, thereby accelerating the discovery of transition metal based materials for these vital applications. [1,2] References: [1] Tanay Sahu, Paul O’Brien*, Kulbir Ghuman*, Harvesting Surface Charges on Metals for Energy-Efficient CO2 Capture: A First-Principles Investigation, Sustainable Materials and Technologies 39, e00843 (2024). [2] Zhao Li, Chengliang Mao, Qijun Pei, Paul N. Duchesne, Teng He, Meikun Xia, Jintao Wang, Lu Wang, Rui Song, Abdinoor A. Jelle, Débora Motta Meira, Kulbir K. Ghuman*, Le He, Xiaohong Zhang*, Geoffrey A. Ozin*, Engineered Disorder in CO2 Photocatalysis, Nature Communication 13, 7205 (2022). 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 Digital Research Alliance of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".