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

By Han Wang, Linfeng Zhang, Jiequn Han, E. Weinan. Computer Physics Communications, 2017

1,831 citations recorded by the source corpus.

Abstract Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics. Potential applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient. On the other end, DeePMD-kit is interfaced with high-performance classical molecular dynamics and quantum (path-integral) molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus, upon training, the potential energy and force field models can be used to perform efficient molecular simulations for different purposes. As an example of the many potential applications of the package, we use DeePMD-kit to learn the interatomic potential energy and forces of a water model using data obtained from density functional theory. We demonstrate that the resulted molecular dynamics model reproduces accurately the structural information contained in the original model. Program summary Program Title: DeePMD-kit Program Files doi: http://dx.doi.org/10.17632/hvfh9yvncf.1 Licensing provisions: LGPL Programming language: Python/C++ Nature of problem: Modeling the many-body atomic interactions by deep neural network models. Running molecular dynamics simulations with the models. Solution method: The Deep Potential for Molecular Dynamics (DeePMD) method is implemented based on the deep learning framework TensorFlow. Supports for using a DeePMD model in LAMMPS and i-PI, for classical and quantum (path integral) molecular dynamics are provided. Additional comments including Restrictions and Unusual features: The code defines a data protocol such that the energy, force, and virial calculated by different third-party molecular simulation packages can be easily processed and used as model training data.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7de41d2b8140f39c646b20db858d47dac9f87b7f

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Citations in the corpus

Deep Potentials for Materials Science (2022, methodology): “Deep Potentials (DP) [44, 45] are of the NNP type that were introduced in 2018.”
Emerging materials intelligence ecosystems propelled by machine learning (2020, methodology): “Different software packages (such as RuNNer 130 , GAP suite 145 , AMP 146 , AGNI 125 , DeepMD 147 , AENet 139 , MTP 148 , SchNetPack 149 , N2P2 (reF.”
Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport (2021, methodology): “…a few case studies to evaluate the performance of NEP implemented in gpumd , as compared to the quip [15] package that implements the GAP-SOAP potential [7, 30], the mlip package [16] that implements the MTP potential [17], and the DeePMD-kit package [18] that implements the DP potential [19, 20].”
Recent advances and applications of deep learning methods in materials science (2021, background): “Such FF models have been developed for molecular systems such as water, methane and other organic molecules [102, 112] as well as solids such as silicon [111], sodium [114], graphite [115] and titania (TiO2) [116].”
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems (2018, methodology): “We used the DeePMD-kit package [18] for all training and testing tasks.”