ASE calculator

SevenNet provides an ASE interface via the ASE calculator. Models can be loaded using the following Python code:

from sevenn.calculator import SevenNetCalculator
# The 'modal' argument is required if the model is trained with multi-fidelity learning enabled.
calc_omni = SevenNetCalculator(model='7net-omni', modal='mpa')

SevenNet also supports CUDA-accelerated D3 calculations. For more information about D3, follow here)

from sevenn.calculator import SevenNetD3Calculator
calc = SevenNetD3Calculator(model='7net-0', device='cuda')

Use enable_cueq, enable_flash, or enable_oeq to use cuEquivariance, flashTP, or OpenEquivariance for faster inference. For more information about accelerators, follow here

from sevenn.calculator import SevenNetCalculator
calc = SevenNetCalculator(model='7net-0', enable_cueq=True) # or enable_flash=True or enable_oeq=True

If you encounter the error CUDA is not installed or nvcc is not available, please ensure the nvcc compiler is available. Currently, CPU + D3 is not supported.

Various pretrained SevenNet models can be accessed by setting the model variable to predefined keywords like 7net-omni, 7net-mf-ompa, 7net-omat, 7net-l3i5, and 7net-0.

User-trained models can be applied with the ASE calculator. In this case, the model parameter should be set to the checkpoint path from training.

Tip

When ‘auto’ is passed to the device parameter (the default setting), SevenNet utilizes GPU acceleration if available.