GPUMD-Tutorials

This repo contains various tutorials and examples using the GPUMD package and related tools.

List of examples

folder	creator	description
01_Carbon_examples_for_JCP_2022_paper	Penghua Ying	Some examples for Ref. [1]
02_Carbon_density_of_states	Zheyong Fan	Phonon density of states of graphene
03_Carbon_thermal_transport_emd	Zheyong Fan	Thermal transport in graphene from EMD
04_Carbon_thermal_transport_nemd_and_hnemd	Zheyong Fan	Thermal transport in graphene from NEMD and NEMD
05_Carbon_phonon_vibration_viewer	Ting Liang	Visualizing the phonon modes in a type of diamond nanowire.
06_Silicon_phonon_dispersion	Zheyong Fan	Phonon dispersions of silicon.
07_Silicon_thermal_expansion	Zheyong Fan	Thermal expansion of silicon based on classical MD.
08_Silicon_melt	Zheyong Fan	Melting point of silicon from two-phase method.
09_Silicon_diffusion	Zheyong Fan	Diffusion coefficient of liquid silicon from VAC and MSD.
10_Silicon_viscosity	Zheyong Fan	Viscosity of liquid silicon from Green-Kubo.
11_NEP_potential_PbTe	Zheyong Fan	Train a NEP potential model for PbTe.
12_NEP_dipole_QM7B	Nan Xu	Train a NEP dipole model for QM7B database.
13_NEP_polarizability_QM7B	Nan Xu	Train a NEP polarizability model for QM7B database.
14_DP	Ke Xu	Examples demonstrating the use of DP models in GPUMD.
15_Infrared	Nan Xu	Calculating infrared spectrum using dipole autocorrelation function.
16_Deposition	Shiyun Xiong	Creation of amorphous Si structures through atom deposition.
17_Wavepacket	Xin Wu	Phonon wavepacket simulation.
18_FCP_check_force	Zheyong Fan	Demonstration of the usage of the force constant potential (FCP)
19_Solid_Liquid_Coexistence_method	Rui Zhao	Solid-liquid coexistence method for melting point calculation
20_Impact	Rui Zhao	Impact simulation
21_Fatigue	Rui Zhao	Fatigue simulation
22_Gas_Solid	Shuo Zhang	Gas-Solid simulation
23_Surface_Reconstruction	Cheng Qian	Pt surface reconstruction simulation
24 Ionic Conductivity	Zihan Yan	Ionic conductivity of cubic Li7La3Zr2O12

How to run the examples?

• First, compile the GPUMD package by typing make in src/. You will get the executables gpumd and nep in src/.

• Then, go to the directory of an example and type one of the following commands:

  • path/to/gpumd
  • path/to/nep

• By default, the nep executable will use all the visible GPUs in the system. This is also the case for the gpumd executable when using a NEP model. The visible GPU(s) can be set by the following command before running the code:

export CUDA_VISIBLE_DEVICES=[list of GPU IDs]
# examples:
export CUDA_VISIBLE_DEVICES=0 # only use GPU with ID 0
export CUDA_VISIBLE_DEVICES=1 # only use GPU with ID 1
export CUDA_VISIBLE_DEVICES=0,2 # use GPUs with ID 0 and ID 2

If you are using a job scheduling system such as slurm, you can set something as follows

#SBATCH --gres=gpu:v100:2 # using 2 V100 GPUs

We suggest use GPUs of the same type, otherwise a fast GPU will wait for a slower one. The parallel efficiency of the nep executable is high (about 90%) unless you have a very small training data set or batch size. The parallel efficiency of the gpumd executable depends on the number of atoms per GPU. Good parallel efficiency requires this number to be larger than about 1e5.

By default, the system is partitioned along the thickest direction, but one can overwrite this by specifying a partition direction in the following way:

potential YOUR_NEP_MODEL.txt   # use the default partition
potential YOUR_NEP_MODEL.txt x # force to partition along the x direction (the a direction for triclinic box)
potential YOUR_NEP_MODEL.txt y # force to partition along the y direction (the b direction for triclinic box)
potential YOUR_NEP_MODEL.txt z # force to partition along the z direction (the c direction for triclinic box)

References

[1] 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).