Explainable AI-Based Physical Theory for Advanced Materials Design Scientists develop an “extended Landau free energy model” for causal analysis and visualization in nano-magnetic devices with AI and topology. Microscopic materials analysis is essential to achieve desirable performance in next-g
In a recent breakthrough, a research team succeeded in automating the interpretation of microscopic image data of nanoscale magnetic materials using an “extended Landau free energy model” that the team developed using a combination of topology, data science, and free energy.Scientists develop an “extended Landau free energy model” for causal analysis and visualization in nano-magnetic devices with AI and topology.
An image depicting the extended Landau free energy model developed by a research team from Tokyo University of Science, which enables a causal analysis of the magnetization reversal in nanomagnets. Through this model, the team could visualize magnetic domain images effectively and were successful in the inverse designing of nanostructures with low energy requirements.
Scatterplot of the dimensionality reduction results of principle component analysis. Color represents the total energy. The relationship between magnetic domain and total energy is connected in the explainable feature space. Credit: Masato Kotsugi from Tokyo University of Science, Japan The model proposed in this study is expected to contribute to a wide range of applications in the development of spintronic devices, quantum information technology, and Web 3.
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