Description
DPA-3.1-3M is trained using a multitask strategy on the OpenLAM datasets. The benchmark results can be found at the LAMBench website.
Please ensure the usage of the correct code version (v3.1.0) corresponding to this model.
How to use
Installation
To use the pretrained model, you need to first install the corresponding version of DeePMD-kit. You can try easy installation:
pip install torch torchvision torchaudio
pip install git+https://github.com/deepmodeling/deepmd-kit@v3.1.0
For other installation options, please visit the Releases page to download the off-line package for deepmd-kit devel, and refer to the official documentation for off-line installation instructions.
Use the pretrained model
The pretrained model can be used directly for prediction tasks, such as serving as an ASE calculator.
This model is pretrained in a multi-task manner, featuring a unified backbone (referred to as the unified descriptor and the unified fitting net) and an dataset encoding vector for different tasks. For detailed information, refer to the DPA-3 paper.
The first step involves selecting a specific dataset id from the model to make predictions. To list the available fitting nets (model-branch) in this pretrained model, use the following command:
dp --pt show DPA-3.1-3M.pt model-branch
This will generate an output similar to the following:
Available model branches are ['Domains_Alloy', 'Domains_Anode', 'Domains_Cluster',
'Domains_Drug', 'Domains_FerroEle', 'Domains_SSE_PBE', 'Domains_SemiCond', 'H2O_H2O_PD',
'Metals_AlMgCu', 'Metals_Sn', 'Metals_Ti', 'Metals_V', 'Metals_W', 'Others_HfO2',
'Domains_SSE_PBESol', 'Domains_Transition1x', 'Metals_AgAu_PBED3', 'Others_In2Se3',
'MP_traj_v024_alldata_mixu', 'Alloy_tongqi', 'SSE_ABACUS', 'Hybrid_Perovskite',
'solvated_protein_fragments', 'Electrolyte', 'ODAC23', 'Alex2D', 'Omat24', 'SPICE2', 'OC20M',
'OC22', 'Organic_Reactions', 'RANDOM'], where 'RANDOM' means using a randomly initialized
fitting net.
Select a dataset id that closely matches your system. Ensure that the elements in your system are included in the corresponding task if you are conducting direct predictions or simulations. For more information on the pretrained datasets, refer to below.
Assuming you choose H2O_H2O-PD, you can first freeze the model branch from the multi-task pretrained model:
dp --pt freeze -c DPA-3.1-3M.pt -o frozen_model.pth --model-branch H2O_H2O-PD
Then you can use the following Python code for prediction or optimization:
## Compute potential energy
from ase import Atoms
from deepmd.calculator import DP as DPCalculator
dp = DPCalculator("frozen_model.pth")
water = Atoms('H2O', positions=[(0.7601, 1.9270, 1), (1.9575, 1, 1), (1., 1., 1.)], cell=[100, 100, 100])
water.calc = dp
print(water.get_potential_energy())
print(water.get_forces())
## Run BFGS structure optimization
from ase.optimize import BFGS
dyn = BFGS(water)
dyn.run(fmax=1e-6)
print(water.get_positions())
For more advanced usages like fine-tuning and zero-shot inference, please refer to the following model card .