# DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

@article{Wang2018DeePMDkitAD, title={DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics}, author={Han Wang and Linfeng Zhang and Jiequn Han and E Weinan}, journal={Comput. Phys. Commun.}, year={2018}, volume={228}, pages={178-184} }

## 269 Citations

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution.

- ChemistryJournal of chemical theory and computation
- 2021

A new deep potential-range correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase is developed and an active learning procedure for robust neural network training is developed.

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.

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: Hoomd-Blue with TensorFlow

- Computer Science
- 2019

Software that enables integration of a scalable GPU-accelerated molecular mechanics engine, Hoomd-blue, with the machine learning (ML) TensorFlow package should lead to both the design of new models in computational chemistry research and reproducible model specification without requiring recompiling or writing low-level code.

Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture

- Computer Sciencenpj Computational Materials
- 2022

By testing on different molecules and bulk systems, it is shown that the proposed molecular dynamics (MD) methodology is generally-applicable to various MD tasks.

Adaptive coupling of a deep neural network potential to a classical force field.

- PhysicsThe Journal of chemical physics
- 2018

This work makes the DeePMD region embedded in the AMM simulated system as if it were embedded in a system that is fully described by the accurate potential, by using a force interpolation scheme and imposing a thermodynamics force in the transition region.

On application of deep learning to simplified quantum-classical dynamics in electronically excited states

- PhysicsMachine Learning: Science and Technology
- 2021

Deep learning (DL) is applied to simulate non-adiabatic molecular dynamics of phenanthrene, using the time-dependent density functional based tight binding (TD-DFTB) approach for excited states…

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution

- Chemistry
- 2021

We develop a new Deep Potential Range Correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed…

DP Train, then DP Compress: Model Compression in Deep Potential Molecular Dynamics

- Computer Science
- 2021

This work reports a model compression scheme for boosting the performance of the Deep Potential model, a deep learning based PES model, and demonstrates that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems.

KLIFF: A framework to develop physics-based and machine learning interatomic potentials

- Computer ScienceComput. Phys. Commun.
- 2022

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

- Computer SciencePhysical review letters
- 2018

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…

## References

SHOWING 1-10 OF 37 REFERENCES

Deep Potential: a general representation of a many-body potential energy surface

- Computer Science
- 2017

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.

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.

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

- Computer SciencePhysical review letters
- 2018

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…

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.

TensorFlow: A system for large-scale machine learning

- Computer ScienceOSDI
- 2016

The TensorFlow dataflow model is described and the compelling performance that Tensor Flow achieves for several real-world applications is demonstrated.

Caffe: Convolutional Architecture for Fast Feature Embedding

- Computer ScienceACM Multimedia
- 2014

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.

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

- PhysicsComput. Phys. Commun.
- 2014

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.

Scalable molecular dynamics with NAMD

- Computer ScienceJ. Comput. Chem.
- 2005

NAMD is a parallel molecular dynamics code designed for high‐performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high‐end parallel platforms, as well as…