Mat Random Structure Search

Random Structure Search (AIRSS-Style)

Goal

To perform random structure searching (RSS) for a given chemical composition — the approach pioneered by AIRSS (Ab Initio Random Structure Searching, Pickard & Needs 2011). Random crystal structures are generated with sensible geometric constraints, then relaxed with an MLIP to identify low-energy candidates.

[!TIP] This method is complementary to ionic substitution and generative models like MatterGen and DiffCSP++. RSS explores the full potential energy surface without structural bias.

Instructions

1. Generate random structures for the target composition:

   # Env: base-agent
   python .agents/skills/mat-random-structure-search/scripts/generate_random_structures.py \
       --composition NaCl \
       --num_structures 100 \
       --output_dir random_NaCl/
   

   The script will:

   • Sample random space groups from a list of common inorganic crystal space groups
   • Generate random lattice parameters consistent with each crystal system
   • Place atoms at random fractional coordinates
   • Filter structures for minimum interatomic distances
   • Save CIF files and a generation_manifest.json

   Optional parameters:

   • --spacegroups 225,166,62,14 — restrict to specific space groups
   • --volume_min 0.6 --volume_max 1.8 — control volume randomization range
   • --seed 42 — set random seed for reproducibility

2. Relax all structures with an MLIP:

   mcp_mace_relax_structure(
       structure_data="random_NaCl/",
       relax_cell=True,
       fmax=0.02,
       steps=500,
       output_dir="relaxed_NaCl/"
   )
   

   Or with MatGL/FairChem — use the same MLIP consistently.

3. Rank by energy: The lowest-energy relaxed structures are the most promising candidates. Check for duplicate structures using pymatgen's StructureMatcher.

4. Validate top candidates: Compute stability (E_hull) for the best candidates to assess thermodynamic viability.

Examples

Example 1: Search for NaCl ground state

# Env: base-agent
python .agents/skills/mat-random-structure-search/scripts/generate_random_structures.py \
    --composition NaCl \
    --num_structures 100 \
    --seed 42 \
    --output_dir random_NaCl/

Expected: Rocksalt (SG 225) should emerge as the lowest-energy structure after MLIP relaxation.

Example 2: Search for Li₂ZrCl₆ polymorphs

# Env: base-agent
python .agents/skills/mat-random-structure-search/scripts/generate_random_structures.py \
    --composition Li2ZrCl6 \
    --num_structures 200 \
    --spacegroups 12,14,62,148,166,167 \
    --output_dir random_Li2ZrCl6/

Constraints

• Not a DFT method: Unlike true AIRSS, this skill uses MLIPs for relaxation. The accuracy depends on the MLIP's quality for the target chemistry.
• No symmetry enforcement: Generated structures have atoms at random positions (P1). Symmetry emerges only after relaxation.
• Volume range: The default volume range (0.6–1.8× estimated) covers most reasonable crystal packings. Extreme chemistries (e.g., heavy elements, molecular crystals) may need adjusted ranges.
• Scalability: Generation is fast (~100 structures/second), but MLIP relaxation is the bottleneck. For large-scale searches, use batch relaxation via MCP tools.
• Duplicate removal: After relaxation, use StructureMatcher to remove duplicate structures that converge to the same minimum.

References

• Pickard, C. J., & Needs, R. J. (2011). Ab initio random structure searching. Journal of Physics: Condensed Matter, 23(5), 053201. DOI: 10.1088/0953-8984/23/5/053201

Author: Bowen Deng Contact: GitHub @learningmatter-mit