To install requirements:
pip install -r requirements.txt
To train the models in the paper and get their validaiton results, the following notebooks should be executed:
- notebooks/diffusion_generation_train.ipynb
- notebooks/diffusion_modification_train.ipynb
- notebooks/flow_matching_generation_train.ipynb
- notebooks/flow_matching_modification_train.ipynb
To generate structures with trained models, the following notebooks should be executed:
- notebooks/diffusion_generation_inference.ipynb
- notebooks/flow_matching_generation_inference.ipynb
To pre-optmizer the generated structures, the following notebooks should be executed:
- notebooks/pre_optimization.ipynb
| DDPM | DDIM | Flow Matching N(0, 1) | Flow Matching U(0, 1) |
|---|---|---|---|
| 0.8074 | 0.82 | 0.482 | 0.8097 |
| Ordinary Model | Diffusion | Flow Matching |
|---|---|---|
| 0.4148 | 0.3653 | 0.2059 |
| UNet Archetecture | Condition Block |
|---|---|
 |
 |
|── notebooks
│ ├── diffusion_generation_inference.ipynb
│ ├── diffusion_generation_train.ipynb
│ ├── diffusion_modification_train.ipynb
│ ├── flow_matching_generation_inference.ipynb
│ ├── flow_matching_generation_train.ipynb
│ └── flow_matching_modification_train.ipynb
├── requirements.txt
└── src
├── data
│ ├── element.pkl
│ └── elemental_properties31-10-2023.json
├── generation
│ ├── diffusion_generation_loops.py
│ ├── flow_matching_generation_loops.py
│ ├── generation.py
│ └── regression_generation_loops.py
├── inference
│ └── inference_data_generation.py
├── losses.py
├── model
│ ├── fp16_util.py
│ ├── models.py
│ ├── nn.py
│ └── unet.py
├── modification
│ ├── diffusion_modification_loops.py
│ ├── flow_matching_modification_loops.py
│ ├── modification.py
│ └── regression_modification_loops.py
├── py_utils
│ ├── comparator.py
│ ├── crystal_dataset.py
│ ├── sampler.py
│ ├── skmultilearn_iterative_split.py
│ └── stratified_splitter.py
└── utils.py