  Coarse-grained models have proven helpful for simulating complex systems over
long timescales to provide molecular insights into various processes.
Methodologies for systematic parameterization of the underlying energy
function, or force field that describes the interactions among different
components of the system are of great interest for ensuring simulation
accuracy. We present a new method, potential contrasting, to enable efficient
learning of force fields that can accurately reproduce the conformational
distribution produced with all-atom simulations. Potential contrasting
generalizes the noise contrastive estimation method with umbrella sampling to
better learn the complex energy landscape of molecular systems. When applied to
the Trp-cage protein, we found that the technique produces force fields that
thoroughly capture the thermodynamics of the folding process despite the use of
only $\alpha$-Carbons in the coarse-grained model. We further showed that
potential contrasting could be applied over large datasets that combine the
conformational ensembles of many proteins to ensure the transferability of
coarse-grained force fields. We anticipate potential contrasting to be a
powerful tool for building general-purpose coarse-grained force fields.
