GPUMD

What is GPUMD?

• GPUMD stands for Graphics Processing Units Molecular Dynamics. GPUMD is a highly efficient general-purpose molecular dynamic (MD) package fully implemented on graphics processing units (GPUs). It enables training and using a class of machine-learned potentials (MLPs) called neuroevolution potentials (NEPs). See this nep-data Gitlab repo for some of 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

• Go to the directory of an example and type one of the following commands:
  • path/to/gpumd
  • path/to/nep

Colab tutorial

• We provide a Colab Tutorial to show the workflow of the construction of a NEP model and its application in large-scale atomistic simulations for PbTe system. This will run entirely on Google's cloud virtual machine. You can also check other offline tutorials in the examples.

Manual

• https://gpumd.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 interfaces:

Package	link
calorine	https://gitlab.com/materials-modeling/calorine
gpyumd	https://github.com/AlexGabourie/gpyumd
pynep	https://github.com/bigd4/PyNEP
somd	https://github.com/initqp/somd

Authors:

• Before the first release, GPUMD was developed by Zheyong Fan, with help from Ville Vierimaa (Previously Aalto University) and Mikko Ervasti (Previously Aalto University) and supervision from Ari Harju (Previously Aalto University).
• Below is the full list of contributors starting from the first release.

Name	contact
Zheyong Fan	https://github.com/brucefan1983
Alexander J. Gabourie	https://github.com/AlexGabourie
Ke Xu	https://github.com/Kick-H
Ting Liang	https://github.com/Tingliangstu
Jiahui Liu	https://github.com/Jonsnow-willow
Penghua Ying	https://github.com/hityingph
Real Name ?	https://github.com/Lazemare
Real Name ?	https://github.com/initqp
Yanzhou Wang	https://github.com/Yanzhou-Wang
Rui Zhao	https://github.com/grtheaory
Eric Lindgren	https://github.com/elindgren
Junjie Wang	https://github.com/bigd4
Yong Wang	https://github.com/AmbroseWong
Zhixin Liang	https://github.com/liangzhixin-202169
Paul Erhart	https://materialsmodeling.org/
Nan Xu	https://github.com/tamaswells
Shunda Chen	https://github.com/shdchen
Jiuyang Shi	https://github.com/XIX-YANG
Nicklas Österbacka	https://github.com/NicklasOsterbacka
Shuning Pan	https://github.com/psn417

Citations

Reference	cite for what?
[1]	for any work that used GPUMD
[2-3]	virial and heat current formulation
[4]	in-out decomposition and related spectral decomposition
[5]	HNEMD and related spectral decomposition
[6]	force constant potential (FCP)
[7-9]	neuroevolution potential (NEP)
[10]	NEP + ZBL
[11]	NEP + D3 dispersion correction
[12]	MSST integrator for shock wave simulation
[13]	linear-scaling quantum transport

References

[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).

[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).

[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).

[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).

[5] Zheyong Fan, Haikuan Dong, Ari Harju, and Tapio Ala-Nissila, Homogeneous nonequilibrium molecular dynamics method for heat transport and spectral decomposition with many-body potentials, Phys. Rev. B 99, 064308 (2019).

[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).

[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 34 125902 (2022).

[9] Zheyong Fan, Yanzhou Wang, Penghua Ying, Keke Song, Junjie Wang, Yong Wang, Zezhu Zeng, Ke Xu, Eric Lindgren, J. Magnus Rahm, Alexander J. Gabourie, Jiahui Liu, Haikuan Dong, Jianyang Wu, Yue Chen, Zheng Zhong, Jian Sun, Paul Erhart, Yanjing Su, Tapio Ala-Nissila, GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations, The Journal of Chemical Physics 157, 114801 (2022).

[10] Jiahui Liu, Jesper Byggmästar, Zheyong Fan, Ping Qian, and Yanjing Su, Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten, Phys. Rev. B 108, 054312 (2023).

[11] Penghua Ying and Zheyong Fan, Combining the D3 dispersion correction with the neuroevolution machine-learned potential, arXiv:2310.05279 [cond-mat.mtrl-sci].

[12] Jiuyang Shi, Zhixing Liang, Junjie Wang, Shuning Pan, Chi Ding, Yong Wang, Hui-Tian Wang, Dingyu Xing, and Jian Sun, Double-Shock Compression Pathways from Diamond to BC8 Carbon, Phys. Rev. Lett. 131, 146101 (2023).

[13] Zheyong Fan, Yang Xiao, Yanzhou Wang, Penghua Ying, Shunda Chen, and Haikuan Dong, Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials, arXiv:2310.15314 [cond-mat.mtrl-sci].