Adversarial-residual-coarse-graining: Applying machine learning theory to systematic molecular coarse-graining
Abstract
We utilize connections between molecular coarse-graining (CG) approaches and implicit generative models in machine learning to describe a new framework for systematic molecular CG. Focus is placed on the formalism encompassing generative adversarial networks. The resulting method enables a variety of model parameterization strategies, some of which show similarity to previous CG methods. We demonstrate that the resulting framework can rigorously parameterize CG models containing CG sites with no prescribed connection to the reference atomistic system (termed virtual sites); however, this advantage is offset by the lack of a closed-form expression for the CG Hamiltonian at the resolution obtained after integration over the virtual CG sites. Computational examples are provided for cases in which these methods ideally return identical parameters as relative entropy minimization CG but where traditional relative entropy minimization CG optimization equations are not applicable.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 26, 2019
- Source ID
- 10.1063/1.5097559
Entities
People
- Aleksander E P Durumeric
- Gregory A. Voth
Organizations
- Air Force Office of Scientific Research
- National Science Foundation
- Office of Naval Research Global
- University of Chicago