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Kinetically Limited Growth of Dendritic Tin Oxide Thin Films: a Machine Learning Study beyond the Structure Zone Diagram
Malmö University, Faculty of Technology and Society (TS), Department of Materials Science and Applied Mathematics (MTM). Malmö University, Biofilms Research Centre for Biointerfaces (BRCB).ORCID iD: 0000-0003-2303-3676
State Key Laboratory of Advanced Marine Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
Malmö University, Faculty of Technology and Society (TS), Department of Materials Science and Applied Mathematics (MTM).
State Key Laboratory of Advanced Marine Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
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2025 (English)In: Advanced Science, E-ISSN 2198-3844, Vol. 12, no 32Article in journal (Refereed) Published
Abstract [en]

Even after fifty years since its introduction, the empirical Thornton's structure zone diagram remains a valuable tool for predicting thin film microstructure. This diagram is essential for understanding the correlation between synthesis, composition, structure, and physical properties in emerging applications. In this work, we critically appraise this diagram by examining Sn─O thin films grown at room temperature using reactive magnetron sputtering. Based on transmission electron microscopy, Sn0.6O0.4 thin films form dendrites featuring nanosized Sn and SnO grains, rather than columns, which are not captured by the structure zone diagram. Using density functional theory and machine learning, we constructed a model to explain this unusual microstructure on the atomic scale. Kinetically limited surface diffusion yields SnO islands on Sn(001), which constitute the initial stage of dendrite formation. This study provides the potential to devise models for thin film microstructure evolution, enhancing performance in advanced applications, such as green energy generation and storage.

Place, publisher, year, edition, pages
John Wiley and Sons Inc , 2025. Vol. 12, no 32
Keywords [en]
density functional theory, machine learning, nanomaterials, thin films, tin oxides
National Category
Condensed Matter Physics
Identifiers
URN: urn:nbn:se:mau:diva-76853DOI: 10.1002/advs.202504627ISI: 001498583000001PubMedID: 40439599Scopus ID: 2-s2.0-105007009243OAI: oai:DiVA.org:mau-76853DiVA, id: diva2:1966990
Funder
Swedish Research CouncilSwedish Research CouncilAvailable from: 2025-06-11 Created: 2025-06-11 Last updated: 2025-09-05Bibliographically approved

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Music, DenisSadowski, GrzegorzOlsson, Pär A T

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