MACE 0.3.13 Release Notes

🚀 LAMMPS Integration Enhancements

• Added new MLIAP (Machine Learning InterAtomic Potential) interface for LAMMPS, enabling significantly improved performance and flexibility. (Documentation)
• Implemented CuEquivariance support in LAMMPS models for GPU acceleration.
• Added multi-GPU inference support for large-scale molecular dynamics simulations.
• Improved timing and profiling capabilities via environment variable controls.
• New command-line option in create_lammps_model.py to select between libtorch (legacy) and MLIAP formats:

# Convert model to MLIAP format
python -m mace.cli.create_lammps_model model.pt --format=mliap

🧮 Atomic Stresses Computation

• Added support for computing atomic stresses and atomic virials.
• Useful for analyzing local stress distributions in materials simulations.

from mace.calculators import mace_mp
from ase import build

# Create structure with 10 atoms
atoms = build.bulk("Al", "fcc", a=4.05, cubic=True)
atoms = atoms.repeat((2, 2, 2))

calc = mace_mp(device="cpu", compute_atomic_stresses=True)
atoms.set_calculator(calc)

atoms.get_potential_energy()
stress = atoms.get_stress()
stresses = atoms.get_stresses()

print("Stress tensor:\n", stress.shape)
print("Stresses tensor:\n", stresses.shape)

🗝️ Property Keys System Redesign

• Reworked the property keys system for improved flexibility and maintainability.
• Introduced KeySpecification class to manage mappings between data formats.
• Added DefaultKeys enum to standardize access patterns.
• Enhanced error reporting when keys are missing.
• Fully backward compatible with previous key conventions.

🧱 New Foundation Models

• MACE-MATPES: New foundation models finetuned on matpes dataset.
• New PBE model with +U inconsistencies and a new R2SCAN model.
• Better transferability for fine-tuning on domain-specific datasets.

| Model | Method | Energy (meV/atom) | Force (meV/Å) | Stress (GPa) | |------------------|--------|-------------------|----------------|--------------| | M3GNet | PBE | 45 | 177 | 0.898 | | CHGNet | PBE | 32 | 124 | 0.617 | | TensorNet | PBE | 36 | 138 | 0.695 | | MACE | PBE | 34 | 122 | 0.296 | | MACE-MATPES-0 | PBE | 23 | 107 | 0.304 | | M3GNet | r2SCAN | 45 | 208 | 0.982 | | CHGNet | r2SCAN | 27 | 150 | 0.705 | | TensorNet | r2SCAN | 34 | 163 | 0.754 | | MACE-MAPTES-0 | r2SCAN | 19 | 119 | 0.265 |

🧠 Improved Model Head Selection

• Added explicit head selection in the MACE calculator for multi-head models.
• You can now directly specify the head:

# Example of selecting a specific head
calc = MACECalculator(model_path="model.pt", head="DFT")

• Automatically falls back to "default" head if not specified.
• Clear error messages when requested head is unavailable.
• Head-specific configurations supported during inference.

🧪 MACE Fine-Tuning Preselection CLI

MACE a tool for selecting configurations when fine-tuning foundation models.

🔍 Key Features

• Multiple Filtering Strategies:

  • combinations: Only elements in your subset
  • exclusive: Exactly your elements
  • inclusive: All your elements plus potentially others

• Selection Methods:

  • fps (Farthest Point Sampling) for maximum diversity
  • random for uniform random sampling

• Weighting Control:

  • Adjust the importance of pretraining vs. fine-tuning data during selection

💡 Usage Example

python -m mace.cli.fine_tuning_select \
  --configs_pt path/to/pretraining_data.xyz \
  --atomic_numbers "[1, 6, 8]" \
  --num_samples 5000 \
  --filtering_type combinations \
  --output selected_configs.xyz

This functionality is also available directly in run_train.py when using the --atomic_numbers parameter. One can no longer do the filtering from run_train without adding the atomic numbers.

📦 Installation

pip install --upgrade mace-torch

# For CUDA acceleration (CUDA 12)
pip install cuequivariance cuequivariance-torch cuequivariance-ops-torch-cu12

# For CUDA 11
pip install cuequivariance-ops-torch-cu11

For complete usage and documentation, please see our official documentation.
If you encounter any issues, please report them on our GitHub Issues page.

🔄 Full Changelog

Full Changelog: https://github.com/ACEsuit/mace/compare/v0.3.12...v0.3.13