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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

DeePCG: Constructing coarse-grained models via deep neural networks.

Linfeng Zhang¹, Jiequn Han¹, Han Wang²

¹Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA.

The Journal of Chemical Physics

|July 25, 2018

Summary

This summary is machine-generated.

We developed a new method, Deep Coarse-Grained Potential (DeePCG), to create accurate many-body models for molecular simulations. This approach significantly speeds up sampling of coarse-grained variables, as demonstrated with liquid water.

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Area of Science:

Computational chemistry
Materials science
Statistical mechanics

Background:

Coarse-grained models simplify complex molecular systems.
Traditional methods often rely on approximations like two- and three-body potentials.
Accurate many-body interactions are crucial for reliable simulations.

Purpose of the Study:

Introduce a general framework for constructing many-body coarse-grained potentials.
Develop a novel approach without ad hoc approximations.
Enhance the efficiency of molecular simulations.

Main Methods:

Developed the Deep Coarse-Grained Potential (DeePCG) scheme.
Utilized a neural network trained on full atomistic data.
Preserved system symmetries during neural network training.
Applied DeePCG to liquid water using oxygen coordinates.

Main Results:

Achieved highly accurate many-body coarse-grained potentials.
Demonstrated significantly faster sampling of coarse-grained configurations.
Obtained excellent agreement in correlation functions (two-body, three-body, and higher-order) between DeePCG and atomistic models for liquid water.

Conclusions:

DeePCG offers a powerful and generalizable framework for coarse-grained modeling.
The method effectively captures complex many-body interactions.
DeePCG significantly improves simulation efficiency while maintaining high accuracy.