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Youzhi Luo
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add SyMat code
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‎OpenMat/SyMat/README.md

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# SyMat
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# Towards Symmetry-Aware Generation of Periodic Materials
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This is the official implementation of the **SyMat** method proposed in the following paper. Code will be released soon.
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This is the official implementation of the **SyMat** method proposed in the following paper.
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Youzhi Luo, Chengkai Liu, Shuiwang Ji. "[Towards Symmetry-Aware Generation of Periodic Materials](https://arxiv.org/abs/2307.02707)".
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Youzhi Luo, Chengkai Liu, Shuiwang Ji. "[Towards Symmetry-Aware Generation of Periodic Materials](https://openreview.net/forum?id=Jkc74vn1aZ)".
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<p align="center">
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<img src="https://github.com/divelab/AIRS/blob/main/OpenMat/SyMat/assets/symat.png" width="800" class="center" alt=""/>
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<br/>
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</p>
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## Requirements
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We include key packages below. The anaconda environment we used is available in environment.yml.
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* PyTorch >= 1.7.1
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* PyTorch Geometric >= 1.6.3
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* pymatgen == 2020.11.11
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* matminer == 0.7.4
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* smact == 2.3.1
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## Run
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Model training:
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```linux
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python train.py --result_path result/ --dataset perov_5
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```
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Material generation:
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```linux
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python generate.py --model_path /path/to/your/model --dataset perov_5 --num_gen 10000
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```
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## Citation
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```latex
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@article{luo2023towards,
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@inproceedings{
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luo2023towards,
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title={Towards Symmetry-Aware Generation of Periodic Materials},
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author={Luo, Youzhi and Liu, Chengkai and Ji, Shuiwang},
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journal={arXiv preprint arXiv:2307.02707},
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year={2023}
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author={Youzhi Luo and Chengkai Liu and Shuiwang Ji},
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booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
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year={2023},
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url={https://openreview.net/forum?id=Jkc74vn1aZ}
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}
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```
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‎OpenMat/SyMat/assets/symat.png

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‎OpenMat/SyMat/config/__init__.py

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from .perov_5_config_dict import conf as perov_5_conf
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from .carbon_24_config_dict import conf as carbon_24_conf
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from .mp_20_config_dict import conf as mp_20_conf

‎OpenMat/SyMat/config/carbon_24_config_dict.py

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conf = {}
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conf_data = {}
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conf_data['prop_name'] = 'energy_per_atom'
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conf_data['graph_method'] = 'crystalnn'
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conf_model = {}
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conf_enc_backbone = {}
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conf_enc_backbone['cutoff'] = 5.0
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conf_enc_backbone['num_layers'] = 4
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conf_enc_backbone['hidden_channels'] = 128
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conf_enc_backbone['out_channels'] = 256
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conf_enc_backbone['int_emb_size'] = 64
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conf_enc_backbone['basis_emb_size_dist'] = 8
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conf_enc_backbone['basis_emb_size_angle'] = 8
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conf_enc_backbone['basis_emb_size_torsion'] = 8
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conf_enc_backbone['out_emb_channels'] = 256
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conf_enc_backbone['num_spherical'] = 7
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conf_enc_backbone['num_radial'] = 6
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conf_dec_backbone = {}
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conf_dec_backbone['cutoff'] = 5.0
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conf_dec_backbone['num_layers'] = 4
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conf_dec_backbone['hidden_channels'] = 128
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conf_dec_backbone['out_channels'] = 256
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conf_dec_backbone['int_emb_size'] = 64
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conf_dec_backbone['basis_emb_size_dist'] = 8
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conf_dec_backbone['basis_emb_size_angle'] = 8
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conf_dec_backbone['basis_emb_size_torsion'] = 8
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conf_dec_backbone['out_emb_channels'] = 256
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conf_dec_backbone['num_spherical'] = 7
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conf_dec_backbone['num_radial'] = 6
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conf_model['enc_backbone_params'] = conf_enc_backbone
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conf_model['dec_backbone_params'] = conf_dec_backbone
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conf_model['latent_dim'] = 128
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conf_model['num_fc_hidden_layers'] = 1
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conf_model['fc_hidden_dim'] = 256
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conf_model['max_num_atoms'] = 24
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conf_model['max_atomic_num'] = 100
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conf_model['use_gpu'] = True
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conf_model['lattice_scale'] = True
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conf_model['pred_prop'] = False
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conf_model['use_multi_latent'] = True
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conf_model['logvar_clip'] = 6.0
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conf_model['mu_clip'] = 14.0
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conf_model['num_time_steps'] = 50
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conf_model['noise_start'] = 0.01
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conf_model['noise_end'] = 10
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conf_model['cutoff'] = 3.0
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conf_model['max_num_neighbors'] = 5
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conf_model['coord_loss_type'] = 'per_node'
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conf_optim = {'lr': 0.001, 'betas': [0.9, 0.999], 'weight_decay': 0.0}
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conf['kld_weight'] = 0.01
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conf['elem_type_num_weight'] = 1.0
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conf['elem_type_weight'] = 30.0
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conf['elem_num_weight'] = 1.0
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conf['lattice_weight'] = 10.0
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conf['coord_weight'] = 10.0
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conf['max_grad_value'] = 0.5
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conf['data'] = conf_data
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conf['model'] = conf_model
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conf['optim'] = conf_optim
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conf['verbose'] = 1
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conf['batch_size'] = 256
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conf['start_epoch'] = 0
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conf['end_epoch'] = 150
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conf['save_interval'] = 50
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conf['chunk_size'] = 1000
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conf['train_temp'] = [1.0, 1.0, 1.0]
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conf['gen_temp'] = [1.0, 0.7, 1.0, 0.01]
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conf['val_temp'] = [1.0, 1.0, 1.0]
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conf['loss_thre'] = [3.0, 10.0]

‎OpenMat/SyMat/config/mp_20_config_dict.py

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conf = {}
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conf_data = {}
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conf_data['prop_name'] = 'formation_energy_per_atom'
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conf_data['graph_method'] = 'crystalnn'
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conf_model = {}
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conf_enc_backbone = {}
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conf_enc_backbone['cutoff'] = 5.0
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conf_enc_backbone['num_layers'] = 4
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conf_enc_backbone['hidden_channels'] = 128
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conf_enc_backbone['out_channels'] = 256
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conf_enc_backbone['int_emb_size'] = 64
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conf_enc_backbone['basis_emb_size_dist'] = 8
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conf_enc_backbone['basis_emb_size_angle'] = 8
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conf_enc_backbone['basis_emb_size_torsion'] = 8
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conf_enc_backbone['out_emb_channels'] = 256
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conf_enc_backbone['num_spherical'] = 7
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conf_enc_backbone['num_radial'] = 6
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conf_dec_backbone = {}
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conf_dec_backbone['cutoff'] = 5.0
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conf_dec_backbone['num_layers'] = 4
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conf_dec_backbone['hidden_channels'] = 128
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conf_dec_backbone['out_channels'] = 256
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conf_dec_backbone['int_emb_size'] = 64
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conf_dec_backbone['basis_emb_size_dist'] = 8
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conf_dec_backbone['basis_emb_size_angle'] = 8
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conf_dec_backbone['basis_emb_size_torsion'] = 8
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conf_dec_backbone['out_emb_channels'] = 256
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conf_dec_backbone['num_spherical'] = 7
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conf_dec_backbone['num_radial'] = 6
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conf_model['enc_backbone_params'] = conf_enc_backbone
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conf_model['dec_backbone_params'] = conf_dec_backbone
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conf_model['latent_dim'] = 128
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conf_model['num_fc_hidden_layers'] = 1
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conf_model['fc_hidden_dim'] = 256
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conf_model['max_num_atoms'] = 20
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conf_model['max_atomic_num'] = 100
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conf_model['use_gpu'] = True
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conf_model['lattice_scale'] = True
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conf_model['pred_prop'] = False
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conf_model['use_multi_latent'] = True
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conf_model['logvar_clip'] = 6.0
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conf_model['mu_clip'] = 14.0
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conf_model['num_time_steps'] = 50
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conf_model['noise_start'] = 0.01
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conf_model['noise_end'] = 10
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conf_model['cutoff'] = 5.0
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conf_model['max_num_neighbors'] = 5
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conf_model['coord_loss_type'] = 'per_node'
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conf_optim = {'lr': 0.001, 'betas': [0.9, 0.999], 'weight_decay': 0.0}
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conf['kld_weight'] = 0.01
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conf['elem_type_num_weight'] = 1.0
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conf['elem_type_weight'] = 30.0
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conf['elem_num_weight'] = 1.0
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conf['lattice_weight'] = 10.0
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conf['coord_weight'] = 10.0
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conf['max_grad_value'] = 0.5
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conf['data'] = conf_data
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conf['model'] = conf_model
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conf['optim'] = conf_optim
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conf['verbose'] = 1
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conf['batch_size'] = 256
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conf['start_epoch'] = 0
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conf['end_epoch'] = 300
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conf['save_interval'] = 50
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conf['chunk_size'] = 1000
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conf['train_temp'] = [1.0, 1.0, 1.0]
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conf['gen_temp'] = [0.2, 0.7, 0.7, 0.01]
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conf['val_temp'] = [1.0, 1.0, 1.0]
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conf['loss_thre'] = [3.0, 10.0]

‎OpenMat/SyMat/config/perov_5_config_dict.py

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conf = {}
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conf_data = {}
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conf_data['prop_name'] = 'heat_ref'
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conf_data['graph_method'] = 'crystalnn'
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conf_model = {}
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conf_enc_backbone = {}
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conf_enc_backbone['cutoff'] = 5.0
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conf_enc_backbone['num_layers'] = 4
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conf_enc_backbone['hidden_channels'] = 128
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conf_enc_backbone['out_channels'] = 256
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conf_enc_backbone['int_emb_size'] = 64
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conf_enc_backbone['basis_emb_size_dist'] = 8
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conf_enc_backbone['basis_emb_size_angle'] = 8
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conf_enc_backbone['basis_emb_size_torsion'] = 8
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conf_enc_backbone['out_emb_channels'] = 256
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conf_enc_backbone['num_spherical'] = 7
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conf_enc_backbone['num_radial'] = 6
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conf_dec_backbone = {}
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conf_dec_backbone['cutoff'] = 5.0
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conf_dec_backbone['num_layers'] = 4
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conf_dec_backbone['hidden_channels'] = 128
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conf_dec_backbone['out_channels'] = 256
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conf_dec_backbone['int_emb_size'] = 64
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conf_dec_backbone['basis_emb_size_dist'] = 8
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conf_dec_backbone['basis_emb_size_angle'] = 8
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conf_dec_backbone['basis_emb_size_torsion'] = 8
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conf_dec_backbone['out_emb_channels'] = 256
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conf_dec_backbone['num_spherical'] = 7
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conf_dec_backbone['num_radial'] = 6
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conf_model['enc_backbone_params'] = conf_enc_backbone
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conf_model['dec_backbone_params'] = conf_dec_backbone
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conf_model['latent_dim'] = 128
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conf_model['num_fc_hidden_layers'] = 1
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conf_model['fc_hidden_dim'] = 256
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conf_model['max_num_atoms'] = 5
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conf_model['max_atomic_num'] = 100
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conf_model['use_gpu'] = True
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conf_model['lattice_scale'] = True
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conf_model['pred_prop'] = False
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conf_model['use_multi_latent'] = True
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conf_model['logvar_clip'] = 6.0
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conf_model['mu_clip'] = 14.0
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conf_model['num_time_steps'] = 50
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conf_model['noise_start'] = 0.01
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conf_model['noise_end'] = 10
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conf_model['cutoff'] = 4.0
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conf_model['max_num_neighbors'] = 5
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conf_model['coord_loss_type'] = 'per_node'
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conf_optim = {'lr': 0.001, 'betas': [0.9, 0.999], 'weight_decay': 0.0}
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conf['kld_weight'] = 0.01
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conf['elem_type_num_weight'] = 1.0
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conf['elem_type_weight'] = 30.0
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conf['elem_num_weight'] = 1.0
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conf['lattice_weight'] = 10.0
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conf['coord_weight'] = 10.0
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conf['max_grad_value'] = 0.5
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conf['data'] = conf_data
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conf['model'] = conf_model
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conf['optim'] = conf_optim
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conf['verbose'] = 1
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conf['batch_size'] = 256
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conf['start_epoch'] = 0
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conf['end_epoch'] = 700
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conf['save_interval'] = 50
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conf['chunk_size'] = 1000
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conf['train_temp'] = [1.0, 1.0, 1.0]
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conf['gen_temp'] = [1.0, 1.0, 0.01, 0.01]
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conf['val_temp'] = [1.0, 1.0, 1.0]
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conf['loss_thre'] = [3.0, 10.0]

‎OpenMat/SyMat/data/carbon_24/score_norm.txt

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1.416221726685762405e-02
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1.631265692412853241e-02
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1.880759373307228088e-02
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2.152392826974391937e-02
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2.486012130975723267e-02
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2.855351194739341736e-02
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3.294009342789649963e-02
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3.797526657581329346e-02
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4.368689283728599548e-02
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5.029701068997383118e-02
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5.785077437758445740e-02
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6.648117303848266602e-02
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7.635820657014846802e-02
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8.791271597146987915e-02
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1.014059782028198242e-01
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1.168085709214210510e-01
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1.343540549278259277e-01
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1.542949527502059937e-01
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1.780358254909515381e-01
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2.044109553098678589e-01
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2.341400384902954102e-01
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2.705442011356353760e-01
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3.113504648208618164e-01
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3.581326901912689209e-01
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4.116409420967102051e-01
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4.748038947582244873e-01
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5.467808842658996582e-01
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6.323360204696655273e-01
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7.295510768890380859e-01
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8.355088829994201660e-01
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9.589910507202148438e-01
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1.085756182670593262e+00
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1.218799352645874023e+00
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1.359216570854187012e+00
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1.490054607391357422e+00
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1.610905528068542480e+00
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1.727462291717529297e+00
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1.820559620857238770e+00
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1.903970122337341309e+00
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1.964640736579895020e+00
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2.005135536193847656e+00
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2.032685756683349609e+00
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2.044181585311889648e+00
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2.061612367630004883e+00
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2.061758041381835938e+00
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2.062728643417358398e+00
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2.067586660385131836e+00
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2.060790777206420898e+00
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2.067802906036376953e+00
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2.058125495910644531e+00

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