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SPONGE in MindSpore is a high-performance and modularized molecular dynamics simulation library developed by Yiqin Gao group (Peking University and Shenzhen Bay Laboratory) and MindSpore team at Huawei Company. It can efficiently simulate traditional molecular dynamics tasks based on the “graph-kernel-fusion” and “automatic parallelization” features of MindSpore. In the meanwhile, it utilizes the automatic differentiation feature of MindSpore, and introduces machine learning methods, such as neural network, into traditional molecular simulation, achieving methodological inventions.
This example demonstrates how to perform high-performance molecular dynamics simulations with the built-in SPONGE module of MindSpore on GPU.
There are three inputs for the example, property file NVT_290_10ns.in, topology file ala.parm7 and coordinates file ala_NVT_290_10ns.out, respectivelly.
Topology file and coordinates file can be generated by tleap in AmberTools (link). For more details, please refer to:
After installing MindSpore via the official website, you can start running as follows:
python main.py --i /path/NVT_290_10ns.in --amber_parm /path/ala.parm7 --c /path/ala_350_cool_290.rst7 \
--o /path/ala_NVT_290_10ns.out
path is the path which stores input files.
├── sponge
├── README.md
├── main.py # launch Simulation for SPONGE
├── src
├── bond.py # bond module in SPONGE
├── angle.py # angle module in SPONGE
├── dihedral.py # dihedral module in SPONGE
├── nb14.py # nb14 module in SPONGE
├── Langevin_Liujian_md.py # Langevin_Liujian_md module in SPONGE
├── lennard_jones.py # lennard_jones module in SPONGE
├── md_information.py # save md information module in SPONGE
├── neighbor_list.py # neighbor_list module in SPONGE
├── particle_mesh_ewald.py # particle_mesh_ewald module in SPONGE
├── simulation_initial.py # SPONGE simulation
python main.py --i ./NVT_290_10ns.in --amber_parm ala.parm7 --c ala_350_cool_290.rst7 --o ala_NVT_290_10ns.out
Training result will be stored in the specified file, which ends with ".out".
After training,the results in ala_NVT_290_10ns.out are:
_steps_ _TEMP_ _TOT_POT_ENE_ _BOND_ENE_ _ANGLE_ENE_ _DIHEDRAL_ENE_ _14LJ_ENE_ _14CF_ENE_ _LJ_ENE_ _CF_PME_ENE_
1 293.105 -6117.709 1204.406 7.096 4.491 3.456 44.018 1372.488 -8753.664
...
There are sorts of energy in the output, steps (steps), temperature (TEMP), total energy (TOT_POT_E), bond energy (BOND_ENE), angle energy (ANGLE_ENE), dihedral energy (DIHEDRAL_ENE), non bond enrgy, includes Coulomb force (14CF_ENE) and Leonard-Jones energy (14LJ_ENE), Van der Waals energy (LJ_ENE) and Coulomb force in PME (CF_PME_ENE).
| Parameters | GPU |
|---|---|
| Resource | GPU(Tesla V100 SXM2), Memory 16G |
| uploaded Date | |
| MindSpore Version | 1.2 |
| Training Parameters | step=1 |
| Outputs | numpy file |
| Speed | 0.47 s/step |
| Total time | 4.57 s |
| Scripts | Link |
Please check the official homepage.
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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