Coarse graining molecular dynamics with graph neural networks.

The Journal of Chemical Physics
Brooke E. HusicCecilia Clementi

Abstract

Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an 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 proved 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 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 learn 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 fo...Continue Reading

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Citations

Mar 18, 2021·Journal of Chemical Theory and Computation·Stefan DoerrGianni De Fabritiis
May 5, 2021·Chemical Reviews·Aldo GlielmoAlessandro Laio
May 5, 2021·The Journal of Chemical Physics·Jiang WangCecilia Clementi
Jun 15, 2021·Chemical Science·Spyridon KoutsoukosTom Welton
Sep 3, 2021·The Journal of Chemical Physics·Yaoyi ChenFrank Noé

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