Mace

MACE - Machine Learning Force Fields

Overview

MACE provides fast, accurate universal machine-learning potentials for atomistic simulations.

When to Use MACE vs. Other Tools

| Scenario | Recommended Tool | Why | |---|---|---| | Fast energy/force evaluation on materials, molecules, or interfaces | MACE | Near-DFT accuracy at a fraction of the cost | | Benchmark-quality energies or electronic properties (DOS, band gaps) | DFT (PySCF/ASE-DFT) | MLIPs do not predict electronic structure | | Very large systems with well-established chemistry | Classical force fields (GROMACS/LAMMPS) | Lower overhead and longer time scales |

Key rule: Use MACE when you need quantum-like accuracy for geometries and energies but do not need electronic properties.

Installation

uv pip install mace-torch

Core Capabilities

1. Energy and Forces

python scripts/run_mace.py water.xyz --task energy --model small --device cpu

2. Geometry Optimization

python scripts/run_mace.py water.xyz --task opt --model small --fmax 0.01 --device cpu

Output

Results are printed as JSON to stdout.

Anti-Patterns (Do Not Do These)

• Do not use MACE to predict electronic properties. MACE models energies and forces, not densities of states, band gaps, or charges.
• Do not assume MACE works for elements outside its training set. Always check that the model was trained on the elements in your system.
• Do not ignore device mismatch. Running large systems on cuda with insufficient GPU memory will crash; use cpu if unsure.
• Do not accept an optimization with loose fmax for production geometries. Tight convergence (fmax <= 0.01) is standard for reliable structures.

Best Practices

• Use device=cpu unless a compatible GPU with sufficient memory is available.
• The first run downloads the model automatically; ensure internet access or set the cache path via the MACE environment variables.
• Validate MACE predictions against a small DFT or reference dataset for your specific system class before scaling up.
• Start with medium model size for production work; small is good for quick screening and large for maximum accuracy.
• For questions about the latest MACE models, newly supported options, or version-specific syntax changes, use WebFetch on https://mace-docs.readthedocs.io/en/latest/ before answering.

Common Errors and Fixes

| Error / Symptom | Likely Cause | Fix | |---|---|---| | CUDA out of memory | System too large for GPU | Switch to device=cpu or reduce cell size | | Model download fails | No internet or cache path issue | Check connectivity or set MACE_CACHE environment variable | | "Element X not found in model" | Element outside training set | Use a different model or switch to DFT/classical FF | | Optimization does not converge | Poor initial structure or fmax too tight | Visualize structure, fix clashes, or relax fmax slightly | | Energy seems unphysical | Wrong model or mixed units | Verify model tag and input structure units (Å) |

References

• Documentation: https://mace-docs.readthedocs.io/en/latest/
• GitHub: https://github.com/ACESuit/mace
• Paper (arXiv): https://arxiv.org/abs/2206.07697

Getting Live Information

If the user asks about recently added features, new model releases, or version-specific syntax changes, use WebFetch on the official documentation at https://mace-docs.readthedocs.io/en/latest/ before answering.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: WuGroup-XJTLU
• Source: WuGroup-XJTLU/cc-skills-ZhenghaoWu-Group
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.