Foundation Potentials Selection

Goal

Select the appropriate machine learning interatomic potential (MLIP) for a given atomistic simulation task, balancing accuracy, computational cost, and material composition.

Model Selection Guide

[!NOTE] This list is not exhaustive. For a full list of available pre-trained checkpoints, refer to the load_model function documentation for each respective MCP server.

MatGL Models

Environment: matgl-agent

• CHGNet-MatPES-r2SCAN-2025.2.10-2.7M-PES:
  • Use for r2SCAN-level inorganic materials simulation.
  • Recommended when charge information and magnetic moments are involved (e.g., calculating transition metal valence states).
• CHGNet-MPtrj-2023.12.1-2.7M-PES:
  • Use for compatibility with standard Materials Project (GGA/GGA+U) data.
  • Recommended when working with legacy MP data.
• TensorNet-MatPES-r2SCAN-v2025.1-PES:
  • Use for r2SCAN-level inorganic materials simulation.
  • Smaller and faster than CHGNet, suitable for dynamic simulations (MD, NEB, phonons).

FAIRCHEM Models

Environment: fairchem-agent

• uma-s-1p1:
  • Use for organic and inorganic simulations.
  • Note: UMA models are typically slower and more expensive. Avoid for dynamic simulations with systems >500 atoms.
• uma-m-1p1:
  • Use for organic and inorganic simulations with <100 atoms.
• esen-md-direct-all-omol:
  • Use for organic ionic relaxation (ground state calculations).

MACE Models

Environment: mace-agent

• MACE-MH-1:
  • Latest multi-head foundation model. Use as default for most tasks.
  • omat_pbe head (default): General materials, balanced performance.
  • matpes_r2scan head: High-accuracy materials simulation.
  • omol head: Molecular systems, organic chemistry, organometallics.
  • spice_wB97M head: Molecular systems and organic chemistry.
  • oc20_usemppbe head: Surface catalysis, adsorbates.
• MACE-MATPES-r2SCAN-0:
  • Specialized for r2SCAN-level inorganic systems.
• MACE-OMAT-0-small:
  • Small, efficient model for materials.

Selection Criteria

Prioritize criteria in the following order:

0. Check the Local Model Registry (Always First)

Before selecting any foundation model, call search_model_registry to check whether a fine-tuned checkpoint already exists for the target chemical system:

mcp_base_search_model_registry(
    chemical_system="Li-Fe-P-O",   # elements of interest
    max_energy_mae=5.0,            # optional accuracy filter (meV/atom)
)

• If a match is found and checkpoint_exists = True, use that model directly — no foundation model selection or fine-tuning is needed.
• If a match is found but checkpoint_exists = False (file missing), fall through to the criteria below and plan a new fine-tuning run.
• If no match is found, continue with the criteria below to select the best foundation model.

[!TIP] After completing any fine-tuning, always register the new model with register_model so it can be reused in future tasks.

1. User Explicit Request

If the user explicitly mentions a model name or framework (e.g., "MACE model", "fine-tuned MACE", "CHGNet", "UMA"), use that model/framework.

• Detect frameworks from keywords like: "MACE", "CHGNet", "TensorNet", "UMA", "ESEN", "FAIRCHEM", "MatGL".

2. Calculation Expense

If the simulation involves dynamic or expensive calculations (Molecular Dynamics, NEB, Phonons, Diffusion, Melting Temperature):

• Prioritize smaller/cheaper models: structure
  • TensorNet-MatPES-r2SCAN-v2025.1-PES
  • MACE-MATPES-r2SCAN-0 (or MACE small variants)
• Avoid UMA models for dynamic simulations due to higher cost, unless the system is very small.

3. System Composition

Consider the chemical elements present in the system:

• Organic (C, H, N, O, P, S):
  • Use UMA models or MACE-MH-1 with omol head.
• Inorganic:
  • Use MatGL, MACE models, or UMA with omat head.
• For Phase Diagrams & Thermodynamic Stability:
  • It is highly recommended to use MatPES-r2SCAN trained checkpoints (e.g., CHGNet-MatPES-r2SCAN, MACE-MATPES-r2SCAN). These offer superior energy accuracy for phase stability and bypass messy energy compatibility corrections in GGA (see mat-mp2020-compatibility).

4. Default

For general materials where no specific constraints apply:

• Use MACE-MH-1 with omat_pbe head.

Performance Benchmark

For detailed inference speed and memory usage of various MLIPs, refer to the dedicated ml-mlip-speed skill. This skill provides automatic benchmarks to help you choose the most efficient model for your simulation scale.

Author: Bowen Deng Contact: GitHub @learningmatter-mit