Researchers at Tokyo University of Science developed an innovative AI model to predict dendritic growth in thin films. These tree-like structures pose a challenge to thin-film device fabrication. By integrating persistent homology and machine learning with energy analysis, the team uncovered hidden growth mechanisms influencing dendritic branching. This method provides valuable insights into growth mechanisms and a data-driven approach to optimizing thin-film fabrication. This breakthrough could lead to higher-quality thin-film devices for high-speed communication systems beyond 5G. The study offers a versatile approach to material analysis, paving the way for advancements in sensor technology, nonequilibrium physics, and high-performance materials.
https://www.tus.ac.jp/en/mediarelations/archive/20250320_5263.html