AI reveals secrets of dendritic growth
Dendritic structures that emerge during the growth of thin films are a major obstacle in large-area fabrication, a key step towards commercialisation.
However, current methods of studying dendrites involve crude visual inspection and subjective analysis. Moreover, growth optimisation methods for controlling dendrite formation require extensive trial and error.
Now, a research team, led by Masato Kotsugi from the Department of Material Science and Technology at Tokyo University of Science (TUS), Japan, have developed a new AI model that incorporates topology analysis and free energy to reveal the specific conditions and mechanisms that drive dendrite branching.
The team included Misato Tone, also from TUS, and Ippei Obayashi from Okayama University. The team developed a novel method that bridges structure and process in dendritic growth by integrating persistent homology and machine learning with energy analysis. “Our approach provides new insights into growth mechanisms and offers a powerful, data-driven pathway for optimising thin-film fabrication,” explains Kotsugi. Their study was published online in Science and Technology of Advanced Materials: Methods on March 7, 2025.
To analyse the morphology of dendrite structures, the team used a cutting-edge topology method called persistent homology (PH). PH enables multiscale analysis of holes and connections within geometric structures, capturing the complex topological features of the tree-like dendrite microstructures that conventional image processing techniques often overlook.
The researchers combined PH with principal component analysis (PCA), a machine learning technique. Through PCA, the essential features of the dendrite morphology extracted via PH were reduced to a two-dimensional space. This enabled the team to quantify structural changes in dendrites and establish a relationship between these changes and Gibbs free energy, or the energy available in a material that influences how dendrites form during crystal growth.
By analysing this relationship, they uncovered the specific conditions and hidden growth mechanisms that influence dendritic branching. Kotsugi explains, “Our framework quantitatively maps dendritic morphology to Gibbs free energy variations, revealing energy gradients that drive branching behavior.”
To validate their approach, the researchers studied dendrite growth in a hexagonal copper substrate and compared their results with data from phase-field simulations.
“By integrating topology and free energy, our method offers a versatile approach to material analysis. Through this integration, we can establish a hierarchical connection between atomic-scale microstructures and macroscopic functionalities across a wide range of materials, paving the way for future advancements in material science,” remarks Prof. Kotsugi. “Importantly, our method could lead to the development of high-quality thin-film devices leading to high-speed communication beyond 5G.”
This study’s framework could pave the way for breakthroughs in sensor technology, nonequilibrium physics, and high-performance materials by uncovering hidden structure-function relationships and advancing complex system analysis.
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