Deep coarse-grained potentials via relative entropy minimization

Stephan Thaler, Maximilian Stupp, Julija Zavadlav

Abstract:

Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their many-body capacity allows highly accurate
approximations of the potential of mean force, promising CG simulations of unprecedented accuracy. CG NN potentials trained bottom-
up via force matching (FM), however, suffer from finite data effects: They rely on prior potentials for physically sound predictions outside
the training data domain, and the corresponding free energy surface is sensitive to errors in the transition regions. The standard alterna-
tive to FM for classical potentials is relative entropy (RE) minimization, which has not yet been applied to NN potentials. In this work,
we demonstrate, for benchmark problems of liquid water and alanine dipeptide, that RE training is more data efficient, due to accessing
the CG distribution during training, resulting in improved free energy surfaces and reduced sensitivity to prior potentials. In addition, RE
learns to correct time integration errors, allowing larger time steps in CG molecular dynamics simulation, while maintaining accuracy. Thus,
our findings support the use of training objectives beyond FM, as a promising direction for improving CG NN potential’s accuracy and
reliability.