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 ...

In materials science, if you can understand the "texture" of a material—how its internal patterns form and shift—you can ...

The team developed a computational framework using robotic path planning algorithms to rapidly identify optimal composition gradients between dissimilar materials. The framework enables the creation ...

Rendered model showing a complex 3D structure made up of thousands of repeating microscopic patterns. By teaching an AI model to predict the behavior of these patterns, the Drexel team can design ...

Tools for simulating manufacturing encompass various levels of CAD, CAM, CAE, PDM, and PLM tools. Solutions are usually tightly integrated vertically within the vendor's own environment, with ...

A recent review article published in Advanced Materials explored the potential of artificial intelligence (AI) and machine learning (ML) in transforming thermoelectric (TE) materials design. The ...

How is healthcare embracing sustainability? Healthcare Design magazine shares recent articles showcasing trends in ...