Coarse graining enables the investigation of molecular dynamics for larger
systems and at longer timescales than is possible at atomic resolution.
However, a coarse graining model must be formulated such that the conclusions
we draw from it are consistent with the conclusions we would draw from a model
at a finer level of detail. It has been proven that a force matching scheme
defines a thermodynamically consistent coarse-grained model for an atomistic
system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)]
demonstrated that the existence of such a variational limit enables the use of
a supervised machine learning framework to generate a coarse-grained force
field, which can then be used for simulation in the coarse-grained space. Their
framework, however, requires the manual input of molecular features upon which
to machine learn the force field. In the present contribution, we build upon
the advance of Wang et al.and introduce a hybrid architecture for the machine
learning of coarse-grained force fields that learns their own features via a
subnetwork that leverages continuous filter convolutions on a graph neural
network architecture. We demonstrate that this framework succeeds at
reproducing the thermodynamics for small biomolecular systems. Since the
learned molecular representations are inherently transferable, the architecture
presented here sets the stage for the development of machine-learned,
coarse-grained force fields that are transferable across molecular systems.

通过引入混合架构来学习分子动力学中的粗粒化力场，以便于能够将粗粒化力场转移到分子系统之间，从而进一步加速模拟的过程。