skphy / gpumd

Graphics Processing Units Molecular Dynamics

GPUMD

What is GPUMD?

• GPUMD stands for Graphics Processing Units Molecular Dynamics. It is a general-purpose molecular dynamics (MD) code fully implemented on graphics processing units (GPUs).
• Force evaluation for many-body potentials has been significantly accelerated by using GPUs [1], thanks to a set of simple expressions for force, virial stress, and heat current derived in Refs. [2, 3].
• Apart from being highly efficient, another unique feature of GPUMD is that it has useful utilities to study heat transport [2, 3, 4, 5].
• It can run MD simulations with the machine-learning based force constant potential (FCP) [6].
• It can train the NEP machine-learning potential [7, 8] and run MD simulations with it. See this nep-data Gitlab repo for all the published NEP potentials and the related training/testing data.

Prerequisites

• You need to have a GPU card with compute capability no less than 3.5 and a CUDA toolkit no older than CUDA 9.0.
• Works for both Linux (with GCC) and Windows (with MSVC) operating systems.

Compile GPUMD

• Go to the src directory and type make. When the compilation finishes, two executables, gpumd and nep, will be generated in the src directory.

Run GPUMD

• See the examples/readme.md file.

Manual

• We only maintain the online manual now: https://gpumd.zheyongfan.org

Mailing list:

• You can use the following link to subscribe and unsubscribe the mailing list: https://www.freelists.org/list/gpumd

• To post a question, you can send an email to gpumd(at)freelists.org

• Here is the archive (public): https://www.freelists.org/archive/gpumd/

Python interface:

• One of the developers, Alexander J. Gabourie, has written a Python package for pre-processing and post-processing data related to GPUMD. Here is the link: https://github.com/AlexGabourie/thermo

Authors:

• Zheyong Fan (Bohai University and Aalto University; Active developer)
  • brucenju(at)gmail.com
• Alexander J. Gabourie (Stanford University; Active developer)
  • gabourie(at)stanford.edu
• Ville Vierimaa (Aalto University; Not an active developer any more)
• Mikko Ervasti (Aalto University; Not an active developer any more)
• Ari Harju (Aalto University; Not an active developer any more)

Citations

Mandatory citation for any work used GPUMD:

• If you use GPUMD in your published work, we kindly ask you to cite the following paper which describes the central algorithms used in GPUMD:

[1] Zheyong Fan, Wei Chen, Ville Vierimaa, and Ari Harju. Efficient molecular dynamics simulations with many-body potentials on graphics processing units. Computer Physics Communications 218, 10 (2017). https://doi.org/10.1016/j.cpc.2017.05.003

Optional citation to the code repository:

• If you want to cite a link to the GPUMD code you can cite the current Github page: https://github.com/brucefan1983/GPUMD.
• However, if the journal does not accept this citation, you can check the Zenodo page of GPUMD (https://zenodo.org/record/4037256#.X2jkqWj7SUk) and cite the version you used. Each version has a unique DOI, which is very suitable for citation. Remember to change the author list to Zheyong Fan and Alexander J. Gabourie.

Other possible citations

• If your work involves using heat current and virial stress formulas as implemented in GPUMD, the following two papers can be cited:

[2] Zheyong Fan, Luiz Felipe C. Pereira, Hui-Qiong Wang, Jin-Cheng Zheng, Davide Donadio, and Ari Harju. Force and heat current formulas for many-body potentials in molecular dynamics simulations with applications to thermal conductivity calculations. Phys. Rev. B 92, 094301, (2015). https://doi.org/10.1103/PhysRevB.92.094301

[3] Alexander J. Gabourie, Zheyong Fan, Tapio Ala-Nissila, Eric Pop, Spectral Decomposition of Thermal Conductivity: Comparing Velocity Decomposition Methods in Homogeneous Molecular Dynamics Simulations, Phys. Rev. B 103, 205421 (2021).

• You can cite the following paper if you use GPUMD to study heat transport using the in-out decomposition for 2D materials and/or the spectral decomposition method as described in it:

[4] Zheyong Fan, Luiz Felipe C. Pereira, Petri Hirvonen, Mikko M. Ervasti, Ken R. Elder, Davide Donadio, Tapio Ala-Nissila, and Ari Harju. Thermal conductivity decomposition in two-dimensional materials: Application to graphene. Phys. Rev. B 95, 144309, (2017). https://doi.org/10.1103/PhysRevB.95.144309

• You can cite the following paper if you use GPUMD to study heat transport using the HNEMD method and the associated spectral decomposition method:

[5] Z. Fan, H. Dong, A. Harju, T. Ala-Nissila, Homogeneous nonequilibrium molecular dynamics method for heat transport and spectral decomposition with many-body potentials, Phys. Rev. B 99, 064308 (2019). https://doi.org/10.1103/PhysRevB.99.064308

• If you use the force constant potential (FCP), you can cite the following paper:

[6] Joakim Brorsson, Arsalan Hashemi, Zheyong Fan, Erik Fransson, Fredrik Eriksson, Tapio Ala-Nissila, Arkady V. Krasheninnikov, Hannu-Pekka Komsa, Paul Erhart, Efficient calculation of the lattice thermal conductivity by atomistic simulations with ab-initio accuracy, Advanced Theory and Simulations 4, 2100217 (2021).

• If you train or use a NEP potential, you can cite the following papers:

[7] Zheyong Fan, Zezhu Zeng, Cunzhi Zhang, Yanzhou Wang, Keke Song, Haikuan Dong, Yue Chen, and Tapio Ala-Nissila, Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport, Phys. Rev. B. 104, 104309 (2021).

[8] Zheyong Fan, Improving the accuracy of the neuroevolution machine learning potentials for multi-component systems, Journal of Physics: Condensed Matter (2021).