Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics

@article{Zhang2018DeepPM,
  title={Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics},
  author={Linfeng Zhang and Jiequn Han and Han Wang and Roberto Car and E Weinan},
  journal={Physical review letters},
  year={2018},
  volume={120 14},
  pages={
          143001
        }
}
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 neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate… 
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References

SHOWING 1-10 OF 50 REFERENCES
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.
A universal strategy for the creation of machine learning-based atomistic force fields
TLDR
A general and universal strategy for using machine learning-based methods to generate highly accurate atomic force fields that may provide a pathway for performing efficient molecular dynamics simulations on nanometer-sized systems over several nanoseconds.
Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.
We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, as derived from quantum mechanical…
Fast and accurate modeling of molecular atomization energies with machine learning.
TLDR
A machine learning model is introduced to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only, and applicability is demonstrated for the prediction of molecular atomization potential energy curves.
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
i-PI: A Python interface for ab initio path integral molecular dynamics simulations
Machine learning of accurate energy-conserving molecular force fields
TLDR
The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.
Accelerating the convergence of path integral dynamics with a generalized Langevin equation.
TLDR
This work describes how a similar approach can be used to accelerate the convergence of path integral (PI) molecular dynamics to the exact quantum mechanical result in more strongly anharmonic systems exhibiting both zero point energy and tunnelling effects.
Generalized neural-network representation of high-dimensional potential-energy surfaces.
TLDR
A new kind of neural-network representation of DFT potential-energy surfaces is introduced, which provides the energy and forces as a function of all atomic positions in systems of arbitrary size and is several orders of magnitude faster than DFT.
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
...
1
2
3
4
5
...