novomcp-nnp¶

Neural network potentials for fast geometry optimization and energy prediction. Three backends: AIMNet2, MACE, and ANI-2x. GPU-accelerated; CPU works but is 10–50× slower.

Pre-reqs¶

Docker
NVIDIA GPU recommended (L4 / A10G / L40S / A100 all fine)
CPU-only fallback for small molecules and low-throughput use
~2 GB RAM per active calculation
~1 GB disk for weights (bundled)

Deploy¶

# GPU
docker run -d \
  --name novomcp-nnp \
  --gpus all \
  -p 8032:8032 \
  --restart unless-stopped \
  ghcr.io/NovoMCP/novomcp-nnp:latest

# CPU only
docker run -d \
  --name novomcp-nnp \
  -p 8032:8032 \
  --restart unless-stopped \
  ghcr.io/NovoMCP/novomcp-nnp:latest

Wire into the engine¶

export NOVOMCP_NNP_URL=http://localhost:8032

Verify¶

curl -s http://localhost:8032/health
# {"status":"healthy","backends":["aimnet2","mace","ani-2x"],"gpu_available":true}

Tools that light up¶

optimize_geometry_nnp, fast NNP geometry optimization
Backend selection is a tool argument (backend: "aimnet2" | "mace" | "ani-2x"); default is aimnet2

Env vars¶

Var	Default	Purpose
`PORT`	`8032`	HTTP listen port
`DEFAULT_BACKEND`	`aimnet2`	Backend when caller doesn't specify
`USE_GPU`	`auto`	Force `cpu` or `gpu`; `auto` detects at boot

Backend cheatsheet¶

AIMNet2, best all-round accuracy for organics containing H/C/N/O/S/F/Cl. Recommended default.
MACE, best for periodic systems and materials. Slightly slower than AIMNet2 for small molecules.
ANI-2x, fast, covers H/C/N/O/S/F/Cl. Slightly less accurate than AIMNet2 but wider validation on drug-like molecules.

Speed¶

AIMNet2 opt (drug-sized molecule): ~0.5 s (GPU) / ~10 s (CPU)
Batch of 1000 molecules: ~2 min (GPU)