Advancing molecular simulation with equivariant interatomic potentials

Deep learning has the potential to accelerate atomistic simulations, but existing models suffer from a lack of robustness, sample efficiency, and accuracy. Simon Batzner, Albert Musaelian, and Boris Kozinsky outline how exploiting the symmetry of Euclidean space offers a new way to address these challenges. Simon Batzner, Albert Musaelian and Boris Kozinsky discuss how exploiting the symmetry of Euclidean space can help tackle challenges in molecular simulations.

Automated summary

Deep learning has potential in accelerating atomistic simulations, but current models lack robustness, sample efficiency, and accuracy. Simon Batzner, Albert Musaelian, and Boris Kozinsky outline how leveraging the symmetry of Euclidean space can address these challenges.