Issue 35, 2020

Graph neural network based coarse-grained mapping prediction


The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice and there is a need for theory. The current state-of-the art method is mapping operators manually selected by experts. In this work, we demonstrate an automated approach by viewing this problem as supervised learning where we seek to reproduce the mapping operators produced by experts. We present a graph neural network based CG mapping predictor called Deep Supervised Graph Partitioning Model (DSGPM) that treats mapping operators as a graph segmentation problem. DSGPM is trained on a novel dataset, Human-annotated Mappings (HAM), consisting of 1180 molecules with expert annotated mapping operators. HAM can be used to facilitate further research in this area. Our model uses a novel metric learning objective to produce high-quality atomic features that are used in spectral clustering. The results show that the DSGPM outperforms state-of-the-art methods in the field of graph segmentation. Finally, we find that predicted CG mapping operators indeed result in good CG MD models when used in simulation.

Graphical abstract: Graph neural network based coarse-grained mapping prediction

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Article information

Article type
Edge Article
30 Apr 2020
10 Aug 2020
First published
11 Aug 2020
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2020,11, 9524-9531

Graph neural network based coarse-grained mapping prediction

Z. Li, G. P. Wellawatte, M. Chakraborty, H. A. Gandhi, C. Xu and A. D. White, Chem. Sci., 2020, 11, 9524 DOI: 10.1039/D0SC02458A

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