| --- |
| license: mit |
| tags: [materials-science, crystal-structure-prediction, diffusion, alignn, ablation] |
| library_name: alignn |
| --- |
| |
| # ALIGNN-CSP angular-diffusion ablations — checkpoints |
|
|
| Trained checkpoints for every cell of the line-graph × bond-angle-diffusion |
| ablation of ALIGNN-CSP (a conditional diffusion model for crystal structure |
| prediction), plus two partial checkpoints from the earlier A-suite's cancelled |
| Alexandria port. Source, harness, analysis and full provenance: |
| https://github.com/crhysc/alignn-csp-ablation (read `PROJECT_STATE.md` there). |
|
|
| ## Layout |
|
|
| ``` |
| lgmatrix/<dataset>/<arm>_seed0/ |
| best_model.pt EMA weights at the epoch of minimum validation structural loss (what was benchmarked) |
| config.json every hyperparameter the run was trained with, incl. angle_mode and n_parameters |
| history.json per-epoch train/val losses (total, lattice, frac, angle, structural) |
| metrics_sym.json AtomBench metrics, symmetrised post-relaxation pipeline |
| metrics_nosym.json AtomBench metrics, unsymmetrised post-relaxation pipeline |
| generation_config.json exact generate_benchmark.py arguments |
| ABLATION.yaml the exhaustive per-run record (what it is, how it differs, jobs, hardware, sha256s) |
| angle-ablation/alex/<arm>_seed0/ partial (cancelled at epoch <=375) A0 and A1, for the record only |
| ``` |
|
|
| `<dataset>` is `jarvis` (JARVIS-DFT Supercon-3D, 847/105/103) or `alex` |
| (Alexandria DS-A/DS-B, 6603/825/825). `<arm>`: |
|
|
| | arm | angular tier | angle_mode | line graph | |
| |---|---|---|---| |
| | nolg | none | off | no (9 pair convs) | |
| | A0 | none | off | yes (3 ALIGNN + 3 pair) | |
| | nolg_ad | derived (legacy) | derived_aux | no | |
| | A3 | derived (legacy) | derived_aux | yes | |
| | nolg_b3 | independent | independent | no | |
| | B3 | independent | independent | yes | |
| |
| ## Loading |
| |
| ```python |
| from alignn.inverse.sample import load_model |
| model, schedule, normalizer, cfg = load_model("lgmatrix/alex/B3_seed0/best_model.pt", device="cuda", use_ema=True) |
| ``` |
| |
| with the `alignn` package from https://github.com/crhysc/alignn at branch |
| `lg-angle-diffusion-matrix` (commit `f8121f4` or later; the `independent` |
| cells need that branch). |
| |
| ## What these were trained with |
| |
| AdamW, lr 1e-3 one-cycle, batch 64, hidden 256, T=1000, σ∈[0.005,0.5], |
| cosine ᾱ, loss weights (lattice, frac, angle) = (1, 10, 1), EMA 0.999, seed |
| 0, checkpoint selected on validation structural loss. Full protocol and the |
| reading of the results: `EXPERIMENT_SET.yaml` in the harness directory of the |
| GitHub repository. |
| |
| ## Caveat |
| |
| Every benchmark number shipped beside these weights was scored **after** |
| ALIGNN-FF relaxation of 32 candidates. The generator-alone scores are the |
| project's open task (see `NEXT_TASK.md` in the repository). |
| |
