Computational Design of Two-dimensional Materials for Electrocatalysis
Bibliographic record
Abstract
Computational Design of Two-dimensional Materials for ElectrocatalysisSamira Siahrostami aa Associate Professor, Department of Chemistry, Simon Fraser UniversityMaterials for Sustainable Development Conference (MATSUS)Proceedings of MATSUS Spring 2024 Conference (MATSUS24)#MatInter - Materials and Interfaces for emerging electrocatalytic reactionsBarcelona, Spain, 2024 March 4th - 8thOrganizers: Marta Costa Figueiredo and María Escudero-EscribanoInvited Speaker, Samira Siahrostami, presentation 316DOI: https://doi.org/10.29363/nanoge.matsus.2024.316Publication date: 18th December 2023Electrocatalysis is at the heart of emerging renewable energy technologies such as fuel cells, electrolyzes, and rechargeable metal-air batteries, all of which are expected to play a significant role in transitioning to a more sustainable future. Two-dimensional materials have emerged as promising electrocatalysts with a wide range of application in electrocatalysis involving oxygen, carbon and nitrogen reactions. In this talk, I will present our recent progress on atomic scale design of two-dimensional materials for various electrocatalysis reactions. More specifically, I focus on computational catalyst design for 1) 2e- oxygen reduction reaction (ORR) for hydrogen peroxide (H2O2) synthesis1-4, and 2) CO2 reduction reaction (CO2RR)5-7. In the first part, I show various strategies that we have applied to tune the activity and selectivity of carbon-based materials for 2e-ORR to enhance the production of H2O2. The main drawback of carbon-based materials is related to their limited performance under acidic conditions which is desired for storage and transportation of H2O2. Extensive experimental work demonstrates that the majority of carbon-based materials are highly active in alkaline conditions and moderately active in neutral conditions. I discuss the computational efforts in understanding the pH effect on the activity of carbon-based structures as well as the insight we can gain from them. In the second part, I describe how we can tune carbon-based materials and two-dimensional metal organic frameworks for CO2RR as well as the insights we acquired from computational analysis. References:[1] Lu, Z.; Chen, G.; Siahrostami, S.; Chen, Z.; Liu, K.; Xie, J.; Liao, L.; Lin D.; Liu, Y.; Jaramillo, T.F.; Nørskov, J. K.; Cui, Y.[2] Jiang, K.; Back, S.; Akey, A.J.; Chuan, X.; Hu, Y.; Liang, W.; Schaak, D; Stavitski, E.; Nørskov, J.K.; Siahrostami, S.; Wang, H.[3] Han, G.; Li, F.; Zou, W.; Karamad, M.; Jeon, J.; Kim, W.; Kim, S.; Bu, Y.; Fu, Z.; Lu, Y.; Siahrostami, S.; Baek, J.[4] Chang, Q.; Zhang, P.; Mostaghimi, A.H.B.; Zhao, X.; Denny, S.R.; Lee, J.H.; Gao, H.; Zhang, Y.; Xin, H.; Siahrostami, S.; Chen, J.G.; Chen, Z.[5] Siahrostami, S.; Jiang, K.; Karamad, M.; Chan, K.; Wang, H.; Nørskov, J.[6] Kirk, C.; Chen, L. D.; Siahrostami, S.; Karamad, M.; Bajdich, M.; Voss, J.; Nørskov. J. K.; Chan, K.[7] Jiang, K.*; Siahrostami, S.*; Zheng, T.; Hu, Y.; Hwang, S.; Stavitski, E.; Peng, Y.; Dynes, J.; Gangisetty, M.; Su, D.; Attenkofer, K.; Wang, H.© FUNDACIO DE LA COMUNITAT VALENCIANA SCITOnanoGe is a prestigious brand of successful science conferences that are developed along the year in different areas of the world since 2009. 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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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".