Kinetically Limited Growth of Dendritic Tin Oxide Thin Films: a Machine Learning Study beyond the Structure Zone Diagram

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, Sn_0.6O_0.4 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.