Data-driven science represents a transformative paradigm in materials science. Both data-driven materials science and informatics encompass systematic knowledge extraction from materials datasets.

For decades, the construction industry followed a familiar rhythm: design came first, materials followed. The pressing need for sustainable buildings has shattered this routine. Material selection is ...

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 ...

Materials informatics sits at the intersection of experimental science, computation, and data analytics. The aim is simple: use data and models to make discovering, designing, and deploying new ...

Align privacy and security objectives with business needs Embed privacy by design and default Engage stakeholders early and maintain transparency throughout the process Minimize data collection and ...