Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

A significant challenge in the development of functional materials is understanding the
growth and transformations of anisotropic colloidal metal nanocrystals. Theory and …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

The discovery of materials is increasingly guided by quantum‐mechanical crystal‐structure
prediction, but the structural complexity in bulk and nanoscale materials remains a …