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:
Paper and Repositories
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.
