HEAForge v0.1.0

HEAForge is a multi-property-guided, crystal-aware generative framework for 100-atom high-entropy alloy (HEA) structures. The model represents bounded relaxations around explicit FCC, BCC, and HCP parent sites and writes final periodic structures as CIF and VASP CONTCAR files.

Code: https://github.com/AgnonZhang/HEAForge

Model Bundle

File	Role	Epoch	Global step	Size
models/vae/heaforge_vae_v9.ckpt	Parent-site residual VAE	253	10,160	614,505,820 bytes
models/predictor/heaforge_predictor_v9.ckpt	Sixteen-property predictor	40	410	453,752 bytes
models/ldm/heaforge_ldm_v9.ckpt	Crystal-conditioned latent diffusion model	144	5,800	115,964,558 bytes

SHA-256 values are provided in checksums.sha256 and structured metadata is provided in manifest.yaml.

Training Data

The final crystal-conditioned stage used 790 valid HEA structures, split with parent-lattice stratification:

• Train: 632
• Validation: 79
• Test: 79

Parent classes are FCC, BCC, and HCP. Every final training structure contains 100 sites. Raw structures and physical-property tables are not distributed in this model repository.

Conditioning

The diffusion model uses sixteen physical-property conditions:

1. Magnetic moment per atom
2. Band gap
3. d-band center
4. d-band width
5. Fermi level
6. DOS at the Fermi level
7. Magnetic susceptibility
8. Conductivity divided by relaxation time
9. Seebeck coefficient
10. Hall coefficient
11. Mixing enthalpy
12. Valence electrons per unit cell
13. Electronic thermal conductivity divided by relaxation time
14. Heat capacity
15. Lattice volume distortion
16. Lattice angle distortion

It also conditions on the parent lattice, six cell parameters, and six strain components. Formation energy is not a target in this release.

Download

hf download Aegon567/HEAForge --local-dir checkpoints/heaforge

Inference

Inference requires a locally prepared HEA condition CSV. See the code repository for data preparation details.

python scripts/3d_mag/generate_direct_contcars.py \
  --checkpoint checkpoints/heaforge/models/ldm/heaforge_ldm_v9.ckpt \
  --vae-checkpoint checkpoints/heaforge/models/vae/heaforge_vae_v9.ckpt \
  --condition-csv data/3d-mag-crystal/train.csv \
  --total-samples 20 \
  --property-condition-mode sampled-row \
  --crystal-condition-mode sampled \
  --output-dir outputs/heaforge_samples

Intended Use

The bundle is intended for HEA crystal candidate generation, method development, and research screening. Generated structures should be subjected to independent geometry relaxation, stability analysis, and electronic- structure calculations before downstream scientific use.

Limitations

• CIF labels are requested conditioning targets, not DFT-verified properties of the generated structure.
• The current model is specialized to 100-atom FCC/BCC/HCP-derived HEA structures and should not be treated as a general inorganic crystal model.
• A valid periodic geometry does not establish thermodynamic or dynamic stability.
• Chemical composition cardinality is not guaranteed unless an explicit composition constraint or post-generation filter is applied.
• Predictions outside the empirical training-property range are not validated.

Origin and License

HEAForge is derived from the MIT-licensed Chemeleon2 framework and retains its VAE, latent diffusion, and reinforcement-learning foundations. HEAForge adds HEA-specific data processing, multi-property conditioning, parent-site residual representations, crystal geometry objectives, and direct CIF/CONTCAR generation.