mlipcpp

WARNING: Pre-alpha software This project is in early development. APIs may change without notice, and there may be bugs or incomplete features. Not recommended for production use.

Standalone C++ implementation of Machine Learning Interatomic Potentials (MLIPs) using ggml.

Currently supports PET/uPET models (energy, forces, stresses).

Quick start (Python)

# Install the package
pip install .

# Download and convert a model to GGUF
uv run scripts/convert_models.py --models pet-mad-s

import numpy as np
import mlipcpp

# Load a model
model = mlipcpp.Predictor("gguf/pet-mad-s.gguf")
print(f"Model type: {model.model_type}, cutoff: {model.cutoff} A")

# Water molecule
positions = np.array([
    [0.000,  0.000, 0.000],  # O
    [0.757,  0.586, 0.000],  # H
    [-0.757, 0.586, 0.000],  # H
], dtype=np.float32)
atomic_numbers = np.array([8, 1, 1], dtype=np.int32)

# Predict energy
result = model.predict(positions, atomic_numbers, compute_forces=False)
print(f"Energy: {result.energy:.4f} eV")
# => Energy: -14.3693 eV

# Predict energy + forces
result = model.predict(positions, atomic_numbers, compute_forces=True)
print(f"Energy: {result.energy:.4f} eV")
forces = np.array(result.forces).reshape(-1, 3)
print(f"Forces (eV/A):\n{forces}")

ASE integration

from ase.io import read
from mlipcpp.ase import MLIPCalculator

atoms = read("structure.xyz")
atoms.calc = MLIPCalculator("gguf/pet-mad-s.gguf")
print(f"Energy: {atoms.get_potential_energy():.4f} eV")

Converting models

Download and convert uPET models from HuggingFace to GGUF format:

# Convert all available models
uv run scripts/convert_models.py

# Convert a specific model
uv run scripts/convert_models.py --models pet-mad-s

# List available models
uv run scripts/convert_models.py --list

Default models: pet-mad-s, pet-oam-l, pet-omad-xs, pet-omad-s, pet-omat-xs, pet-omat-s, pet-spice-s

Use --all to also convert larger variants: pet-oam-xl, pet-omad-l, pet-omat-m, pet-omat-l, pet-omat-xl, pet-omatpes-l, pet-spice-l

Building from source

Dependencies

• ggml - Tensor library (fetched automatically via CMake)
• fmt - Formatting library (fetched automatically)

Note: This project uses a modified fork of ggml with additional backpropagation support for CONCAT and CLAMP operations, required for force/stress computation.

Build

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build . -j

C++ CLI

# Energy only
./build/bin/graph_inference gguf/pet-mad-s.gguf structure.xyz

# With forces
./build/bin/graph_inference gguf/pet-mad-s.gguf structure.xyz --forces

# With forces and stress (periodic systems)
./build/bin/graph_inference gguf/pet-mad-s.gguf structure.xyz --forces --stress

API

C, C++, Fortran, and Python APIs are provided. See examples/ for usage.

License

BSD 3-Clause