Disentangled variational autoencoder (D-VAE) separates materials properties from the latent space by conditioning to make inverse materials design more efficient and transparent. It combines labeled and unlabeled data for improved efficiency and provides (IMAGE)

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Disentangled variational autoencoder (D-VAE) separates materials properties from the latent space by conditioning to make inverse materials design more efficient and transparent. It combines labeled and unlabeled data for improved efficiency and provides

Caption

Disentangled variational autoencoder (D-VAE) separates materials properties from the latent space by conditioning to make inverse materials design more efficient and transparent. It combines labeled and unlabeled data for improved efficiency and provides insights into why specific materials are chosen.

Credit

Cheng Zeng, the Roux Institute at Northeastern University

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