  Molecular dynamics (MD) simulations allow atomistic insights into chemical
and biological processes. Accurate MD simulations require computationally
demanding quantum-mechanical calculations, being practically limited to short
timescales and few atoms. For larger systems, efficient, but much less reliable
empirical force fields are used. Recently, machine learned force fields (MLFFs)
emerged as an alternative means to execute MD simulations, offering similar
accuracy as ab initio methods at orders-of-magnitude speedup. Until now, MLFFs
mainly capture short-range interactions in small molecules or periodic
materials, due to the increased complexity of constructing models and obtaining
reliable reference data for large molecules, where long-ranged many-body
effects become important. This work proposes a general approach to constructing
accurate MLFFs for large-scale molecular simulations (GEMS) by training on
"bottom-up" and "top-down" molecular fragments of varying size, from which the
relevant physicochemical interactions can be learned. GEMS is applied to study
the dynamics of alanine-based peptides and the 46-residue protein crambin in
aqueous solution, allowing nanosecond-scale MD simulations of >25k atoms at
essentially ab initio quality. Our findings suggest that structural motifs in
peptides and proteins are more flexible than previously thought, indicating
that simulations at ab initio accuracy might be necessary to understand dynamic
biomolecular processes such as protein (mis)folding, drug-protein binding, or
allosteric regulation.
