v0.3.13

MACE 0.3.13 Release Notes

🚀 LAMMPS Integration Enhancements

• Added new MLIAP 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 using MPI message passing of intermediate tensors for effecient scaling.
• 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

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🧮 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)

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🗝️ 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.

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🧱 New Foundation Models

• MACE-MATPES: New foundation models finetuned on matpes dataset.
• New PBE model without +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

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🧠 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.

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🧪 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.

🧠 L-BFGS Training Support

MACE now supports L-BFGS optimization for enhanced convergence in energy and force training. This second-order optimizer can refine models beyond what first-order methods like Adam achieve.

🚀 Key Points

• Ideal for final-stage training (1–2 epochs) after Adam
• Often achieves lower energy errors and better balance of energy vs. force loss
• Supports multi-GPU training
• Slower per epoch but may require fewer total epochs

🛠️ Usage

First, pretrain with Adam:

python -m mace.cli.run_train --optimizer adam [...other options...]

Then refine with L-BFGS:

python -m mace.cli.run_train --lbfgs --restart_latest [...other options...]

ℹ️ Uses history_size=200 and max_iter=20 with strong Wolfe line search.
Requires more memory as it processes the full dataset per update.

📦 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.

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🔄 Full Changelog

Full Changelog: v0.3.12...v0.3.13