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

@article{Zhang2018DeePCGCC,
  title={DeePCG: Constructing coarse-grained models via deep neural networks.},
  author={Linfeng Zhang and Jiequn Han and Han Wang and Roberto Car and Weinan E},
  journal={The Journal of chemical physics},
  year={2018},
  volume={149 3},
  pages={
          034101
        }
}
We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- and/or three-body contributions. The scheme, called the Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a carefully crafted neural network to construct a many-body coarse-grained potential. The network is trained with full atomistic data in a way that preserves the natural symmetries of the system. The resulting model is very… 
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References

SHOWING 1-10 OF 57 REFERENCES
Deep Potential: a general representation of a many-body potential energy surface
TLDR
Deep Potential is able to reproduce the original model, whether empirical or quantum mechanics based, within chemical accuracy, and the computational cost of this new model is not substantially larger than that of empirical force fields.
Neural Network Based Prediction of Conformational Free Energies - A New Route toward Coarse-Grained Simulation Models.
TLDR
A new method where a neural network is used to extract high-dimensional free energy surfaces (FES) from molecular dynamics simulation trajectories and it is shown that the NN not only is able to correctly describe the free-energy surface for oligomer lengths that it was trained on but also was able to predict the conformational sampling of longer chains.
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep…
Quantum-chemical insights from deep tensor neural networks
TLDR
An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.
Reinforced dynamics for enhanced sampling in large atomic and molecular systems. I. Basic Methodology
TLDR
A new approach for efficiently exploring the configuration space and computing the free energy of large atomic and molecular systems is proposed, motivated by an analogy with reinforcement learning, which allows for an efficient exploration of the configurationspace by adding an adaptively computed biasing potential to the original dynamics.
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
TLDR
Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models is developed.
The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models.
TLDR
The present work develops a formal statistical mechanical framework for the MS-CG method and demonstrates that the variational principle underlying the method may, in principle, be employed to determine the many-body potential of mean force (PMF) that governs the equilibrium distribution of positions of the CG sites for theMS-CG models.
The multiscale coarse-graining method. IX. A general method for construction of three body coarse-grained force fields.
TLDR
These calculations show that the inclusion of three body terms in the nonbonded CG potential can lead to significant improvement in the accuracy of CG potentials and hence of CG simulations.
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials.
We introduce a computational framework that is able to describe general many-body coarse-grained (CG) interactions of molecules and use it to model the free energy surface of molecular liquids as a…
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