# 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…

## Figures and Tables from this paper

## 492 Citations

Towards exact molecular dynamics simulations with invariant machine-learned models

- Computer Science
- 2019

This thesis develops a combined machine learning (ML) and quantum mechanics approach that enables the direct reconstruction of flexible molecular force fields from high-level ab initio calculations and provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.

Towards exact molecular dynamics simulations with machine-learned force fields

- ChemistryNature Communications
- 2018

A flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations is developed, for flexible molecules with up to a few dozen atoms and insights into the dynamical behavior of these molecules are provided.

Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach

- Chemistry, PhysicsFrontiers in Molecular Biosciences
- 2022

Molecular mechanics (MM) is a powerful tool to study the properties of molecular systems in the fields of biology and materials science. With the development of ab initio force field and the…

Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

- Computer Science
- 2021

A computational approach is presented that combines machine learning with recent advances in path integral contraction schemes, and this work achieves a two-order-of-magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.

Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

- Computer Science
- 2021

A computational approach is presented that combines machine learning with recent advances in path integral contraction schemes, and a two orders-of-magnitude acceleration is achieved over direct path integral AIMD simulation while at the same time maintaining its accuracy.

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

- Computer Sciencenpj Computational Materials
- 2021

It is demonstrated that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system.

Graph Neural Networks Accelerated Molecular Dynamics

- Computer ScienceThe Journal of chemical physics
- 2022

A GNN Accelerated MD (GAMD) model is developed that directly predicts forces, given the state of the system, bypassing the evaluation of potential energy and is agnostic to the scale, where it can scale to much larger systems at test time.

Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture

- Computer Science
- 2020

It is demonstrated that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system.

Isotope effects in liquid water via deep potential molecular dynamics

- PhysicsMolecular Physics
- 2019

A comprehensive microscopic understanding of ambient liquid water is a major challenge for ab initio simulations as it simultaneously requires an accurate quantum mechanical description of the…

Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms

- Computer SciencePPoPP
- 2022

This work opens the door for unprecedentedly large-scale molecular dynamics simulations based on ab initio accuracy and can be potentially utilized in studying more realistic applications such as mechanical properties of metals, semiconductor devices, batteries, etc.

## References

SHOWING 1-10 OF 50 REFERENCES

Quantum-chemical insights from deep tensor neural networks

- ChemistryNature communications
- 2017

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

- Physicsnpj Computational Materials
- 2017

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.

- PhysicsPhysical review letters
- 2010

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.

- Computer SciencePhysical review letters
- 2012

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

- Computer ScienceComput. Phys. Commun.
- 2018

i-PI: A Python interface for ab initio path integral molecular dynamics simulations

- PhysicsComput. Phys. Commun.
- 2014

Machine learning of accurate energy-conserving molecular force fields

- ChemistryScience Advances
- 2017

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.

- PhysicsThe Journal of chemical physics
- 2011

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.

- Computer SciencePhysical review letters
- 2007

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

- PhysicsJ. Comput. Phys.
- 2015