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Supervised AI and Deep Neural Networks to Evaluate High-Entropy Alloys as Reduction Catalysts in Aqueous Environments
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Materials Science and Engineering, Solid State Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Materials Science and Engineering, Solid State Physics. Newcastle Univ, Sch Nat & Environm Sci, Energy Mat Lab, Newcastle Upon Tyne NE1 7RU, England..ORCID iD: 0000-0003-2759-7356
2024 (English)In: ACS Catalysis, E-ISSN 2155-5435, Vol. 14, no 6, p. 3742-3755Article in journal (Refereed) Published
Abstract [en]

Competitive surface adsorption energies on catalytic surfaces constitute a fundamental aspect of modeling electrochemical reactions in aqueous environments. The conventional approach to this task relies on applying density functional theory, albeit with computationally intensive demands, particularly when dealing with intricate surfaces. In this study, we present a methodological exposition of quantifying competitive relationships within complex systems. Our methodology leverages quantum-mechanical-guided deep neural networks, deployed in the investigation of quinary high-entropy alloys composed of Mo-Cr-Mn-Fe-Co-Ni-Cu-Zn. These alloys are under examination as prospective electrocatalysts, facilitating the electrochemical synthesis of ammonia in aqueous media. Even in the most favorable scenario for nitrogen fixation identified in this study, at the transition from O and OH coverage to surface hydrogenation, the probability of N2 coverage remains low. This underscores the fact that catalyst optimization alone is insufficient for achieving efficient nitrogen reduction. In particular, these insights illuminate that system consideration with oxygen- and hydrogen-repelling approaches or high-pressure solutions would be necessary for improved nitrogen reduction within an aqueous environment.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2024. Vol. 14, no 6, p. 3742-3755
Keywords [en]
machine learning, deep neural networks, high-entropyalloys, scaling relations, competitive data analysis, DFT
National Category
Materials Chemistry
Identifiers
URN: urn:nbn:se:uu:diva-528882DOI: 10.1021/acscatal.3c05017ISI: 001174410500001PubMedID: 38510666OAI: oai:DiVA.org:uu-528882DiVA, id: diva2:1863090
Funder
Swedish National Infrastructure for Computing (SNIC), 2019-05591Swedish Research CouncilVinnovaAvailable from: 2024-05-30 Created: 2024-05-30 Last updated: 2024-07-04Bibliographically approved

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Araujo, RafaelEdvinsson, Tomas

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