Learning local equivariant representations for large-scale atomistic dynamics.

ABSTRACT: A simultaneously accurate and computationally efficient parametrization of the potential energy surface of molecules and materials is a long-standing goal in the natural sciences. While atom-centered message passing neural networks (MPNNs) have shown remarkable accuracy, their information propagation has limited the accessible length-scales. Local methods, conversely, scale to large simulations but have suffered from inferior accuracy. This work introduces Allegro, a strictly local equivariant deep neural network interatomic potential architecture that simultaneously exhibits excellent accuracy and scalability. Allegro represents a many-body potential using iterated tensor products of learned equivariant representations without atom-centered message passing. Allegro obtains improvements ov

SUBMITTER: Musaelian A 

PROVIDER: S-EPMC9898554 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature