Force-matching Coarse-Graining without Forces

• URL: http://arxiv.org/abs/2203.11167v1
• Date: Mon, 21 Mar 2022 17:46:35 GMT
• Title: Force-matching Coarse-Graining without Forces
• Authors: Jonas K\"ohler, Yaoyi Chen, Andreas Kr\"amer, Cecilia Clementi, Frank No\'e
• Abstract summary: Learning CG force fields from all-atom data has mainly relied on force-matching and relative entropy minimization. We present emphflow-matching, a new training method for CG force fields that combines the advantages of force-matching and relative entropy minimization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time-~and length-scales inaccessible to all-atom simulations. Learning CG force fields from all-atom data has mainly relied on force-matching and relative entropy minimization. Force-matching is straightforward to implement but requires the forces on the CG particles to be saved during all-atom simulation, and because these instantaneous forces depend on all degrees of freedom, they provide a very noisy signal that makes training the CG force field data inefficient. Relative entropy minimization does not require forces to be saved and is more data-efficient, but requires the CG model to be re-simulated during the iterative training procedure, which can make the training procedure extremely costly or lead to failure to converge. Here we present \emph{flow-matching}, a new training method for CG force fields that combines the advantages of force-matching and relative entropy minimization by leveraging normalizing flows, a generative deep learning method. Flow-matching first trains a normalizing flow to represent the CG probability density by using relative entropy minimization without suffering from the re-simulation problem because flows can directly sample from the equilibrium distribution they represent. Subsequently, the forces of the flow are used to train a CG force field by matching the coarse-grained forces directly, which is a much easier problem than traditional force-matching as it does not suffer from the noise problem. Besides not requiring forces, flow-matching also outperforms classical force-matching by an order of magnitude in terms of data efficiency and produces CG models that can capture the folding and unfolding of small proteins.

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