ADiT Structure Generation Skill

Goal

Generate novel crystal structures and molecules using ADiT (All-atom Diffusion Transformers, ICML 2025), a unified latent diffusion framework from Meta FAIR Chemistry that jointly generates both periodic materials and non-periodic molecular systems from a shared latent space.

1. Prerequisites

[!IMPORTANT] GPU Required: ADiT requires a CUDA-compatible GPU. CPU inference is extremely slow.

• The adit-agent conda environment must be installed and configured.
• The AADT repository must be cloned to .agents/tmp/adit/.
• Pre-trained weights are automatically downloaded from HuggingFace on first use.

2. Available Models

ADiT provides a joint pre-trained model trained on:

• MP20: Materials Project 2020 dataset (inorganic crystals, ~45K structures)
• QM9: Small organic molecules (~134K molecules)

The single checkpoint handles both crystal and molecule generation, selected via the generation_type parameter.

3. MCP Tool Usage

Crystal Generation

Generate novel periodic crystal structures (saved as CIF files):

mcp_adit_generate_structures(
    generation_type="crystals",    # Generate periodic crystals
    num_structures=10,             # Number of structures to generate
    batch_size=100,                # Batch size for GPU efficiency
    cfg_scale=2.0,                 # Classifier-free guidance scale
    output_dir="research/my_project/crystals"
)

Molecule Generation

Generate novel non-periodic molecules (saved as XYZ files):

mcp_adit_generate_structures(
    generation_type="molecules",   # Generate molecules
    num_structures=10,
    batch_size=100,
    cfg_scale=2.0,
    output_dir="research/my_project/molecules"
)

4. Parameters

Parameter	Default	Description
generation_type	"crystals"	"crystals" for periodic structures (CIF), "molecules" for non-periodic (XYZ)
num_structures	10	Total number of structures to generate
batch_size	100	Batch size (larger = faster on GPU)
cfg_scale	2.0	Classifier-free guidance scale. Higher = more typical but less diverse
device	"auto"	Device: "auto", "cpu", or "cuda"
output_dir	auto	Output directory. Auto-creates under research dir

5. Output Files

Crystal Generation

• crystal_XXXX.cif: Generated crystal structure files (pymatgen CIF format)
• generation_metadata.json: Generation parameters and statistics

Molecule Generation

• molecule_XXXX.xyz: Generated molecule files (ASE XYZ format)
• generation_metadata.json: Generation parameters and statistics

6. Limitations

[!WARNING] No Conditional Generation: The public checkpoint is unconditional only — you cannot condition on specific compositions, space groups, or properties. To get specific compositions: generate many structures and filter.

[!WARNING] No Fine-Tuning via MCP: Fine-tuning requires the full AADT training pipeline with multi-GPU setup and wandb logging. Use the raw codebase for training.

[!NOTE] Atom Count Distribution: The number of atoms per generated structure is sampled from the training dataset distribution. For crystals (MP20), this peaks around 8-20 atoms. For molecules (QM9), this peaks around 18 atoms including hydrogens.

7. Best Practices

[!TIP]

• Start with crystals: Crystal generation on MP20 tends to produce more valid structures
• Guidance scale: Use 2.0 (default) for balanced diversity/quality. Increase to 3.0-4.0 for more "typical" structures
• Validate outputs: Always validate generated structures via relaxation and stability analysis
• Batch size: Use batch_size=100 for best GPU throughput

8. Workflow Integration

ADiT works well in combination with:

• Structure relaxation: Use MLIP tools (MACE, FairChem, MatGL) to optimize generated structures
• Stability analysis: Calculate E_hull to identify thermodynamically stable phases
• Property prediction: Use MLIPs or DFT to calculate properties of generated structures
• Comparison with MatterGen: Generate structures with both ADiT and MatterGen for diversity

9. Architecture

ADiT uses a two-stage latent diffusion approach:

1. VAE Autoencoder: Maps all-atom representations (atoms, coords, lattice) to a shared latent space
2. DiT Denoiser: Trained via flow matching to generate new latent embeddings
3. Decoder: Converts latent embeddings back to atomic structures

This unified framework handles both periodic (crystals) and non-periodic (molecules) systems.

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Author: Bowen Deng Contact: GitHub @learningmatter-mit