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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.
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