pet-mad 1.2.0

A universal interatomic potential for advanced materials modeling

PET-MAD: A Universal Interatomic Potential for Advanced Materials Modeling

This repository contains PET-MAD - a universal interatomic potential for advanced materials modeling across the periodic table. This model is based on the Point Edge Transformer (PET) model trained on the Massive Atomic Diversity (MAD) Dataset and is capable of predicting energies and forces in complex atomistic simulations.

Key Features

• Universality: PET-MAD is a generally-applicable model that can be used for a wide range of materials and molecules.
• Accuracy: PET-MAD achieves high accuracy in various types of atomistic simulations of organic and inorganic systems, comparable with system-specific models, while being fast and efficient.
• Efficiency: PET-MAD achieves high computational efficiency and low memory usage, making it suitable for large-scale simulations.
• Infrastructure: Various MD engines are available for diverse research and application needs.
• HPC Compatibility: Efficient in HPC environments for extensive simulations.

Table of Contents

1. Installation
2. Pre-trained Models
3. Interfaces for Atomistic Simulations
4. Usage
   • ASE Interface
   • Evaluating PET-MAD on a dataset
   • Running PET-MAD with LAMMPS
   • Running PET-MAD with empirical dispersion corrections
5. Examples
6. Fine-tuning
7. Documentation
8. Citing PET-MAD

Installation

You can install PET-MAD using pip:

pip install pet-mad

Or directly from the GitHub repository:

pip install git+https://github.com/lab-cosmo/pet-mad.git

Alternatively, you can install PET-MAD using conda package manager, which is especially important for running PET-MAD with LAMMPS.

[!WARNING] We strongly recommend installing PET-MAD using Miniforge as a base conda provider, because other conda providers (such as Anaconda) may yield undesired behavior when resolving dependencies and are usually slower than Miniforge. Smooth installation and use of PET-MAD is not guaranteed with other conda providers.

To install Miniforge on unix-like systems, run:

wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh

Once Miniforge is installed, create a new conda environment and install PET-MAD with:

conda create -n pet-mad
conda activate pet-mad
conda install -c metatensor -c conda-forge pet-mad

Pre-trained Models

Currently, we provide the following pre-trained models:

• v1.1.0 or latest: The updated PET-MAD model with an ability to run simulations using the non-conservative forces and stresses (temporarily disabled).
• v1.0.1: The updated PET-MAD model with a new, pure PyTorch backend and slightly improved performance.
• v1.0.0: PET-MAD model trained on the MAD dataset, which contains 95,595 structures, including 3D and 2D inorganic crystals, surfaces, molecular crystals, nanoclusters, and molecules.

Interfaces for Atomistic Simulations

PET-MAD integrates with the following atomistic simulation engines:

• Atomic Simulation Environment (ASE)
• LAMMPS (including the KOKKOS support)
• i-PI
• OpenMM (coming soon)
• GROMACS (coming soon)

Usage

ASE Interface

You can use the PET-MAD calculator, which is compatible with the Atomic Simulation Environment (ASE):

from pet_mad.calculator import PETMADCalculator
from ase.build import bulk

atoms = bulk("Si", cubic=True, a=5.43, crystalstructure="diamond")
pet_mad_calculator = PETMADCalculator(version="latest", device="cpu")
atoms.calc = pet_mad_calculator
energy = atoms.get_potential_energy()
forces = atoms.get_forces()

These ASE methods are ideal for single-structure evaluations, but they are inefficient for the evaluation on a large number of pre-defined structures. To perform efficient evaluation in that case, read here.

Evaluating PET-MAD on a dataset

Efficient evaluation of PET-MAD on a desired dataset is also available from the command line via metatrain, which is installed as a dependency of PET-MAD. To evaluate the model, you first need to fetch the PET-MAD model from the HuggingFace repository:

mtt export https://huggingface.co/lab-cosmo/pet-mad/resolve/main/models/pet-mad-latest.ckpt

This command will download the model and convert it to TorchScript format. Then you need to create the options.yaml file and specify the dataset you want to evaluate the model on (where the dataset is stored in extxyz format):

systems: your-test-dataset.xyz
targets:
  energy:
    key: "energy"
    unit: "eV"

Then, you can use the mtt eval command to evaluate the model on a dataset:

mtt eval pet-mad-latest.pt options.yaml --batch-size=16 --extensions-dir=extensions --output=predictions.xyz

This will create a file called predictions.xyz with the predicted energies and forces for each structure in the dataset. More details on how to use metatrain can be found in the Metatrain documentation.

Running PET-MAD with LAMMPS

1. Install LAMMPS with metatomic support

To use PET-MAD with LAMMPS, you need to first install PET-MAD from conda (see the installation instructions above). Then, follow the instructions here to install lammps-metatomic. We recomend you also use conda to install lammps.

2. Run LAMMPS with PET-MAD

2.1. CPU version

Fetch the PET-MAD checkpoint from the HuggingFace repository:

mtt export https://huggingface.co/lab-cosmo/pet-mad/resolve/main/models/pet-mad-latest.ckpt

This will download the model and convert it to TorchScript format compatible with LAMMPS, using the metatomic and metatrain libraries, which PET-MAD is based on.

Prepare a lammps input file using pair_style metatomic and defining the mapping from LAMMPS types in the data file to elements PET-MAD can handle using pair_coeff syntax. Here we indicate that lammps atom type 1 is Silicon (atomic number 14).

units metal
atom_style atomic

read_data silicon.data

pair_style metatomic pet-mad-latest.pt device cpu # Change device to 'cuda' evaluate PET-MAD on GPU
pair_coeff * * 14

neighbor 2.0 bin
timestep 0.001

dump myDump all xyz 10 trajectory.xyz
dump_modify myDump element Si

thermo_style multi
thermo 1

velocity all create 300 87287 mom yes rot yes

fix 1 all nvt temp 300 300 0.10

run 100

Create the silicon.data data file for a silicon system.

# LAMMPS data file for Silicon unit cell
8 atoms
1 atom types

0.0  5.43  xlo xhi
0.0  5.43  ylo yhi
0.0  5.43  zlo zhi

Masses

1  28.084999992775295 # Si

Atoms # atomic

1   1   0   0   0
2   1   1.3575   1.3575   1.3575
3   1   0   2.715   2.715
4   1   1.3575   4.0725   4.0725
5   1   2.715   0   2.715
6   1   4.0725   1.3575   4.0725
7   1   2.715   2.715   0
8   1   4.0725   4.0725   1.3575

lmp -in lammps.in  # For serial version
mpirun -np 1 lmp -in lammps.in  # For MPI version

2.2. KOKKOS-enabled GPU version

Running LAMMPS with KOKKOS and GPU support is similar to the CPU version, but you need to change the lammps.in slightly and run lmp binary with a few additional flags.

The updated lammps.in file looks like this:

package kokkos newton on neigh half

units metal
atom_style atomic/kk

read_data silicon.data

pair_style metatensor/kk pet-mad-latest.pt # This will use the same device as the kokkos simulation
pair_coeff * * 14

neighbor 2.0 bin
timestep 0.001

dump myDump all xyz 10 trajectory.xyz
dump_modify myDump element Si

thermo_style multi
thermo 1

velocity all create 300 87287 mom yes rot yes

fix 1 all nvt temp 300 300 0.10

run_style verlet/kk
run 100

The silicon.data file remains the same.

To run the KOKKOS-enabled version of LAMMPS, you need to run

lmp -in lammps.in -k on g 1 --suffix kk # For serial version
mpirun -np 1 lmp -in lammps.in -k on g 1 --suffix kk # For MPI version

Here, the -k on g 1 --suffix kk flags are used to activate the KOKKOS subroutines. Specifically g 1 is used to specify, how many GPUs are the simulation is parallelized over, so if running the large systems on two or more GPUs, this number should be adjusted accordingly.

3. Important Notes

• For CPU calculations, use a single MPI task unless simulating large systems (30+ Å box size). Multi-threading can be enabled via:

  export OMP_NUM_THREADS=4
  

• For GPU calculations, use one MPI task per GPU.

Running PET-MAD with empirical dispersion corrections

In ASE:

You can combine the PET-MAD calculator with the torch based implementation of the D3 dispersion correction of pfnet-research - torch-dftd:

Within the PET-MAD environment you can install torch-dftd via:

pip install torch-dftd

Then you can use the D3Calculator class to combine the two calculators:

from torch_dftd.torch_dftd3_calculator import TorchDFTD3Calculator
from pet_mad.calculator import PETMADCalculator
from  ase.calculators.mixing import SumCalculator

device = "cuda" if torch.cuda.is_available() else "cpu"

calc_MAD = PETMADCalculator(version="latest", device=device)
dft_d3 = TorchDFTD3Calculator(device=device, xc="pbesol", damping="bj")

combined_calc = SumCalculator([calc_MAD, dft_d3])

# assign the calculator to the atoms object
atoms.calc = combined_calc

Examples

More examples for ASE, i-PI, and LAMMPS are available in the Atomistic Cookbook.

Fine-tuning

PET-MAD can be fine-tuned using the Metatrain library.

Documentation

Additional documentation can be found in the metatensor, metatomic and metatrain repositories.

• Training a model
• Fine-tuning
• LAMMPS interface
• i-PI interface

Citing PET-MAD

If you use PET-MAD in your research, please cite:

@misc{PET-MAD-2025,
      title={PET-MAD, a universal interatomic potential for advanced materials modeling},
      author={Arslan Mazitov and Filippo Bigi and Matthias Kellner and Paolo Pegolo and Davide Tisi and Guillaume Fraux and Sergey Pozdnyakov and Philip Loche and Michele Ceriotti},
      year={2025},
      eprint={2503.14118},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci},
      url={https://arxiv.org/abs/2503.14118}
}