A recent breakthrough is the use of artificial intelligence (AI), which learns how to manipulate atoms to improve a material’s properties.

Proteins are constantly moving, but our structures of them are static. Clare Sansom talks to the researchers using ...

A scientific visualization project built in Unity to procedurally generate and interact with the crystal structure of Bismuth Ferrite (BiFeO₃). This tool reads atomic coordinate data from a .csv file ...

Despite never observing 3D information during its training, results reported in the preprint indicate that ProtoBind-Diff’s performance matched or exceeded that of structure-based models such as ...

Discover how a new AI model enables the precise atomic structure analysis of nanocrystals, with applications in material science and drug development.

AI Extends the Method to Nanoparticles The team trained a generative AI model on 40,000 known atomic structures to develop a system that is able to make sense of these inferior X-ray patterns.

This paper presents a new efficient technique for large-scale structure from motion from unordered data sets. We avoid costly computation of all pairwise matches and geometries by sampling pairs of ...

Method for analyzing 3D atomic structural changes in nanoparticles using the time-resolved Brownian tomography technique.

Previously, the atomic-scale structure of amorphous materials could only be handled qualitatively, or predicted/estimated using molecular dynamics (MD). Use of the 3D structural visualization ...

The technology succeeds in making visible information on the atomic-scale 3D structure of amorphous carbon, based on measured X-ray scattering data. By making available detailed information on the ...