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Function Decomposition Tree with Causality-First Perspective and Systematic Description of Problems in Materials Informatics
The proposed method was applied to materials informatics to demonstrate the systematic representation of expert knowledge and its usefullness.
Ensemble learning reveals dissimilarity between rare-earth transition metal binary alloys with respect to the Curie temperature
We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property.
Variational Hyper-Encoding Networks
Given a target distribution, we predict the posterior distribution of the latent code, then use a matrix-network decoder to generate a posterior distribution q(\theta).
Measuring the Similarity between Materials with an Emphasis on the Materials Distinctiveness
In this study, we establish a basis for selecting similarity measures when applying machine learning techniques to solve materials science problems.
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