Coarse Graining Molecular Dynamics with Graph Neural Networks

• URL: http://arxiv.org/abs/2007.11412v3
• Date: Fri, 6 Nov 2020 15:57:54 GMT
• Title: Coarse Graining Molecular Dynamics with Graph Neural Networks
• Authors: Brooke E. Husic, Nicholas E. Charron, Dominik Lemm, Jiang Wang, Adri\`a P\'erez, Maciej Majewski, Andreas Kr\"amer, Yaoyi Chen, Simon Olsson, Gianni de Fabritiis, Frank No\'e, Cecilia Clementi
• Abstract summary: We introduce a hybrid architecture for the machine learning of coarse-grained force fields that learns their own features via a subnetwork. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems.
• Score: 3.0279361008741827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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