  Recent years have seen vast progress in the development of machine learned
force fields (MLFFs) based on ab-initio reference calculations. Despite
achieving low test errors, the reliability of MLFFs in molecular dynamics (MD)
simulations is facing growing scrutiny due to concerns about instability over
extended simulation timescales. Our findings suggest a potential connection
between robustness to cumulative inaccuracies and the use of equivariant
representations in MLFFs, but the computational cost associated with these
representations can limit this advantage in practice. To address this, we
propose a transformer architecture called SO3krates that combines sparse
equivariant representations (Euclidean variables) with a self-attention
mechanism that separates invariant and equivariant information, eliminating the
need for expensive tensor products. SO3krates achieves a unique combination of
accuracy, stability, and speed that enables insightful analysis of quantum
properties of matter on extended time and system size scales. To showcase this
capability, we generate stable MD trajectories for flexible peptides and
supra-molecular structures with hundreds of atoms. Furthermore, we investigate
the PES topology for medium-sized chainlike molecules (e.g., small peptides) by
exploring thousands of minima. Remarkably, SO3krates demonstrates the ability
to strike a balance between the conflicting demands of stability and the
emergence of new minimum-energy conformations beyond the training data, which
is crucial for realistic exploration tasks in the field of biochemistry.
