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 ...

A research team including members from the University of Michigan have unveiled a new observational technique that's ...

This invertibility of SLICES allows for the generation of new structures from learned representations, which is key to inverse materials design. As a demonstration, we applied SLICES in the ...

Inverse design is a fast-growing technique for developing new materials and devices that are specialized for highly specific uses. Starting from a desired functionality, inverse-design algorithms work ...

Fungi have been around for many millions of years, with the incremental process of evolution honing and improving their ...

The hot-end components of high-performance high-speed aircraft need to meet performance requirements such as long service life, wide temperature range oxidation resistance and ablation resistance.

Predicting the emergent properties of a material from a microscopic description is a scientific challenge. Machine learning and reverse-engineering have opened new paradigms in the understanding and ...

However, the inverse design of the displacement-sensitive cavity ... The algorithm is fit for discrete variables such as the material index number (each material’s complex refractive index ...

Docent Timo Lähivaara (University of Eastern Finland) hosted the Designs for Noise Reducing Materials and Structures (DENORMS) COST Action’s fourth Workshop “Working days on design and ...