| --- |
| library_name: pytorch |
| tags: |
| - materials-science |
| - crystal-generation |
| - graph-ml |
| - diffusion-model |
| - diffcsp |
| - inverse-design |
| - density-functional-theory |
| - closed-loop-discovery |
| - arxiv:2606.16133 |
| license: other |
| license_name: invdesmobility-research-artifact-license |
| license_link: https://huggingface.co/DreamLufei/invDesMobility-diffcsp-generator/blob/main/LICENSE |
| datasets: |
| - DreamLufei/invDesMobility-data |
| --- |
| |
| # InvDesMobility DiffCSP Generator Checkpoints |
|
|
| This repository contains the DiffCSP generator checkpoints used by the |
| InvDesMobility inverse-design workflow. The checkpoints are released as |
| external artifacts for the code repositories: |
|
|
| - https://github.com/DreamLufei/invDesMobility |
| - https://github.com/DreamLufei/invdesmobility_loop |
| - https://github.com/DreamLufei/2d-mobility |
| |
| ## Paper and Repositories |
| |
| - Paper: InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery |
| - arXiv: https://arxiv.org/abs/2606.16133 |
| - Project page: https://dreamlufei.github.io/invDesMobility/ |
| - GitHub: https://github.com/DreamLufei/invDesMobility |
| - Loop repository: https://github.com/DreamLufei/invdesmobility_loop |
| - Mobility workflow: https://github.com/DreamLufei/2d-mobility |
| - Zenodo: https://doi.org/10.5281/zenodo.20475023 |
|
|
| ## Files |
|
|
| - `pretrained/PretrainGenerationModel.ckpt`: upstream DiffCSP warm-start checkpoint used by the pipeline. |
| - `finetuned/mobility2d_highquality280_ft_v1/best.ckpt`: seed mobility-2D fine-tuned generator. |
| - `finetuned/generator_round_XX/best.ckpt`: closed-loop feedback fine-tuned generators for rounds 01, 02, 03, 04, 06, 07, 08, and 09. |
| - Each fine-tuned directory also includes `hparams.yaml`, `lattice_scaler.pt`, and `prop_scaler.pt`. |
|
|
| The `epoch=...ckpt` files from local training logs are intentionally omitted because |
| they are byte-identical to the retained `best.ckpt` files for the corresponding |
| rounds. Run logs, W&B files, generated pools, and VASP outputs are not included. |
|
|
| ## Intended Use |
|
|
| These checkpoints are intended for reproducing the candidate-generation stage of |
| InvDesMobility and for research use in feedback-guided 2D crystal generation. |
|
|
| ## Training Data |
|
|
| The seed model was fine-tuned on the mobility-2D high-quality seed dataset. |
| Closed-loop checkpoints were fine-tuned on feedback-augmented DiffCSP datasets |
| constructed from trusted first-principles mobility-validation records. |
|
|
| The matching datasets are packaged separately in `DreamLufei/invDesMobility-data`. |
|
|
| ## Training Parameters |
|
|
| The retained `hparams.yaml` files contain the exact DiffCSP/PyTorch Lightning |
| configuration for each checkpoint, including model architecture, dataset path, |
| batch size, optimization configuration, and diffusion scheduler settings. |
|
|
| Key settings used by the feedback models include: |
|
|
| - DiffCSP `CSPDiffusion` with `CSPNet` decoder. |
| - `hidden_dim=512`, `num_layers=6`, `max_neighbors=20`, `radius=7.0`. |
| - Fine-tuning via the project scripts under `05_steps/02_finetune_generator/` and |
| `05_steps/09_closed_loop_feedback/`. |
|
|
| ## Evaluation |
|
|
| These generator checkpoints were evaluated operationally inside the closed-loop |
| InvDesMobility workflow: structures generated from each checkpoint were |
| deduplicated, screened by surrogate models, and then selected candidates were |
| validated with first-principles mobility calculations. |
|
|
| The generated pools, feedback datasets, and retained-channel records used to |
| audit this process are packaged in `DreamLufei/invDesMobility-data`. No |
| standalone generative benchmark table is included in this model repository, |
| because the relevant quality measure for this work is downstream retention and |
| DFT validation rather than raw sample likelihood alone. |
|
|
| ## Limitations |
|
|
| The generator proposes candidate structures; it does not validate mobility, |
| dynamic stability, synthesizability, or DFT-level electronic structure. Candidate |
| structures require downstream deduplication, surrogate screening, and |
| first-principles validation with the companion `2d-mobility` workflow. |
|
|
| ## Citation |
|
|
| If you use these checkpoints, please cite the InvDesMobility manuscript and the |
| associated GitHub repositories above. |
|
|
