Configuration

This page explains how to configure NIM for BGR using environment variables during container launch, apply per-request overrides for geometry relaxation parameters, and manage model and GPU settings.

Environment Variables

Pass environment variables when you launch the container. For example:

docker run ... -e ALCHEMI_NIM_MODEL_TYPE="mace" ...

Use the environment variables in the following table to configure NIM for BGR:

Environment Variable	Type	Default	Description
ALCHEMI_NIM_MODEL_TYPE	string	"mace"	Model type: mace, aimnet2, or tensornet.
ALCHEMI_NIM_MODEL_PATH	string	null	Path to the model file or directory inside the container.
ALCHEMI_NIM_PBC	bool	true	Periodic boundary conditions mode.
ALCHEMI_NIM_BATCH_SIZE	int	null	Fixed batch size. If null, the NIM estimates the batch size automatically at startup.
ALCHEMI_NIM_BGR_OPTTOL	float	0.005	Force tolerance (eV/Å). You can override this per request.
ALCHEMI_NIM_BGR_MAX_STEPS	int	2000	Maximum optimization steps. You can override this per request.
ALCHEMI_NIM_BGR_CELLOPT	bool	false	Enable cell optimization. You can override this per request.
ALCHEMI_NIM_BGR_OPTTOL_PRESSURE	float	0.5	Pressure tolerance (kBar) for cell optimization. You can override this per request.
ALCHEMI_NIM_BGR_OPTIMIZER_PRESET	string	"materials"	Optimizer preset: materials or molecules.
ALCHEMI_NIM_DFT3_ENABLED	bool	false	Enable DFT-D3(BJ) dispersion correction.
ALCHEMI_NIM_DFT3_PARAM	string (JSON)	null	JSON-encoded object for custom DFT-D3(BJ) parameters (for example, {"s8": 0.3908, "a1": 0.5660, "a2": 3.1280}).

Refer to Environment Variables for a complete reference of all environment variables and their defaults.

DFT-D3 Parameters

When ALCHEMI_NIM_DFT3_ENABLED is true, you can provide custom damping parameters using the ALCHEMI_NIM_DFT3_PARAM environment variable. The value must be a JSON string (for example, '{"s8": 0.3908, "a1": 0.5660, "a2": 3.1280}').

For detailed information on DFT-D3(BJ) dispersion corrections, refer to the ALCHEMI Toolkit-Ops documentation.

Field	Type	Default	Description
a1	float	null	BJ damping parameter a1
a2	float	null	BJ damping parameter a2 (Bohr)
s6	float	null	Scaling factor for the C6 term
s8	float	null	Scaling factor for the C8 term
cutoff	float	15.0	Cutoff distance (Å)
smoothing_fraction	float	0.2	Fraction of the cutoff used for the smoothing region

Model Configuration

For supported models and sourcing details, refer to Supported Models. For step-by-step download hints and container launch arguments for each model type (MACE, AIMNet2, TensorNet), refer to Custom Models.

For non-bundled models (TensorNet and AIMNet2), mount the content of the model directory into the container and set ALCHEMI_NIM_MODEL_PATH to the mounted path inside the container:

docker run --rm -ti --name alchemi-bgr --gpus=all \
    -e NGC_API_KEY \
    -p 8000:8000 --shm-size=8g \
    -v /path/to/model-dir:/opt/nim/.cache/model-dir \
    -e ALCHEMI_NIM_MODEL_TYPE="tensornet" \
    -e ALCHEMI_NIM_MODEL_PATH="/opt/nim/.cache/model-dir" \
    nvcr.io/nim/nvidia/alchemi-bgr:${__container_version}

The mounted directory should contain the model files as downloaded from the model repository. For specific model requirements, refer to Custom Models.

GPU and Memory Configuration

Configure memory allocation and GPU utilization to manage hardware resources.

• Shared memory: Use --shm-size=8g to support large requests.

• Batch size: The NIM estimates the optimal and maximum batch size for available GPU memory at startup. Override this behavior by setting ALCHEMI_NIM_BATCH_SIZE.

• Multi-GPU: The NIM automatically uses all available GPUs. Each GPU runs an independent worker.