We introduced two representation in respect to materials: naive representation and expert representation.

We proposed a uniforming method of dimensionality of data with different size input vectors using a neural network.

The proposed method outperformed the conventional methods (the multi-layer autoencoder, the denoising autoencoder, and kernel PCA) for the linear regression task on synthetic data.

The experimental results showed the robustness for data size and number of constituent elements.

In the linear regression task on ion conductivity data of bulk materials and hydrogen storage materials, the good fitting performance was obtained in terms of the latent data uniformed by the proposed method.