Learning local equivariant representations for large-scale atomistic dynamics
The prediction of properties of molecules and materials requires both quantum-chemical accuracy combined with large length- and time-scales. We proposed a deep learning approach that overcomes the limitations of existing methods and provides a step towards increased predictive power of simulation.
