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aimd_result

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  1. .gitattributes +10 -0
  2. ads_0_length_2000/ALLEGRO/results/adsorption/config.yaml +750 -0
  3. ads_0_length_2000/ALLEGRO/results/adsorption/log +148 -0
  4. ads_0_length_2000/ALLEGRO/results/adsorption/metrics_batch_train.csv +446 -0
  5. ads_0_length_2000/ALLEGRO/results/adsorption/metrics_batch_val.csv +0 -0
  6. ads_0_length_2000/ALLEGRO/results/adsorption/metrics_epoch.csv +10 -0
  7. ads_0_length_2000/ALLEGRO/results/adsorption/metrics_initialization.csv +2 -0
  8. ads_0_length_2000/ALLEGRO/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/params.yaml +13 -0
  9. ads_0_length_4000/ALLEGRO/results/adsorption/best_model.pth +3 -0
  10. ads_0_length_4000/ALLEGRO/results/adsorption/last_model.pth +3 -0
  11. ads_0_length_4000/ALLEGRO/results/adsorption/trainer.pth +3 -0
  12. ads_0_length_4000/ALLEGRO/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/pre_filter.pt +3 -0
  13. ads_0_length_4000/ALLEGRO/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/pre_transform.pt +3 -0
  14. ads_0_length_4000/ALLEGRO/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/pre_filter.pt +3 -0
  15. ads_0_length_4000/ALLEGRO/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/pre_transform.pt +3 -0
  16. ads_0_length_4000/MACE/MACE_model.model +3 -0
  17. ads_0_length_4000/MACE/MACE_model_compiled.model +3 -0
  18. ads_0_length_4000/MACE/MACE_model_stagetwo.model +3 -0
  19. ads_0_length_4000/MACE/MACE_model_stagetwo_compiled.model +3 -0
  20. ads_0_length_4000/MACE/checkpoints/MACE_model_run-123.model +3 -0
  21. ads_0_length_4000/MACE/checkpoints/MACE_model_run-123_epoch-0.pt +3 -0
  22. ads_0_length_4000/MACE/checkpoints/MACE_model_run-123_epoch-375_swa.pt +3 -0
  23. ads_0_length_4000/MACE/checkpoints/MACE_model_run-123_stagetwo.model +3 -0
  24. ads_0_length_4000/MACE/results/MACE_model_run-123_train.txt +3 -0
  25. ads_0_length_4000/NEQUIP/results/adsorption/best_model.pth +3 -0
  26. ads_0_length_4000/NEQUIP/results/adsorption/last_model.pth +3 -0
  27. ads_0_length_4000/NEQUIP/results/adsorption/trainer.pth +3 -0
  28. ads_0_length_4000/NEQUIP/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/pre_filter.pt +3 -0
  29. ads_0_length_4000/NEQUIP/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/pre_transform.pt +3 -0
  30. ads_0_length_4000/NEQUIP/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/pre_filter.pt +3 -0
  31. ads_0_length_4000/NEQUIP/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/pre_transform.pt +3 -0
  32. ads_0_length_4000/testset.xyz +3 -0
  33. ads_0_length_4000/trainset.xyz +3 -0
  34. ads_16_length_2000/NEQUIP/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/data.pth +3 -0
  35. ads_16_length_4000/ALLEGRO/results/adsorption/best_model.pth +3 -0
  36. ads_16_length_4000/ALLEGRO/results/adsorption/last_model.pth +3 -0
  37. ads_16_length_4000/ALLEGRO/results/adsorption/trainer.pth +3 -0
  38. ads_16_length_4000/ALLEGRO/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/pre_filter.pt +3 -0
  39. ads_16_length_4000/ALLEGRO/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/pre_transform.pt +3 -0
  40. ads_16_length_4000/ALLEGRO/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/data.pth +3 -0
  41. ads_16_length_4000/ALLEGRO/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/pre_filter.pt +3 -0
  42. ads_16_length_4000/ALLEGRO/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/pre_transform.pt +3 -0
  43. ads_16_length_4000/MACE/checkpoints/MACE_model_run-123.model +3 -0
  44. ads_16_length_4000/MACE/checkpoints/MACE_model_run-123_epoch-0.pt +3 -0
  45. ads_16_length_4000/MACE/checkpoints/MACE_model_run-123_epoch-375_swa.pt +3 -0
  46. ads_16_length_4000/MACE/checkpoints/MACE_model_run-123_stagetwo.model +3 -0
  47. ads_16_length_4000/MACE/results/MACE_model_run-123_train.txt +3 -0
  48. ads_16_length_4000/NEQUIP/results/adsorption/best_model.pth +3 -0
  49. ads_16_length_4000/NEQUIP/results/adsorption/last_model.pth +3 -0
  50. ads_16_length_4000/NEQUIP/results/adsorption/trainer.pth +3 -0
.gitattributes CHANGED
@@ -86,3 +86,13 @@ ads_0_length_6000/trainset.xyz filter=lfs diff=lfs merge=lfs -text
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  ads_0_length_6000/ALLEGRO/results/adsorption/metrics_batch_train.csv filter=lfs diff=lfs merge=lfs -text
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  ads_0_length_6000/ALLEGRO/results_/adsorption/metrics_batch_train.csv filter=lfs diff=lfs merge=lfs -text
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  ads_16_length_4000/testset.xyz filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
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  ads_0_length_6000/ALLEGRO/results/adsorption/metrics_batch_train.csv filter=lfs diff=lfs merge=lfs -text
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  ads_0_length_6000/ALLEGRO/results_/adsorption/metrics_batch_train.csv filter=lfs diff=lfs merge=lfs -text
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  ads_16_length_4000/testset.xyz filter=lfs diff=lfs merge=lfs -text
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+ ads_16_length_4000/trainset.xyz filter=lfs diff=lfs merge=lfs -text
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+ ads_16_length_4000/MACE/results/MACE_model_run-123_train.txt filter=lfs diff=lfs merge=lfs -text
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+ ads_32_length_2000/testset.xyz filter=lfs diff=lfs merge=lfs -text
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+ ads_32_length_2000/trainset.xyz filter=lfs diff=lfs merge=lfs -text
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+ ads_0_length_4000/testset.xyz filter=lfs diff=lfs merge=lfs -text
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+ ads_0_length_4000/trainset.xyz filter=lfs diff=lfs merge=lfs -text
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+ ads_0_length_4000/MACE/results/MACE_model_run-123_train.txt filter=lfs diff=lfs merge=lfs -text
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+ ads_8_length_8000/testset.xyz filter=lfs diff=lfs merge=lfs -text
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+ ads_16_length_6000/testset.xyz filter=lfs diff=lfs merge=lfs -text
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+ ads_8_length_8000/ALLEGRO/results/adsorption/metrics_batch_train.csv filter=lfs diff=lfs merge=lfs -text
ads_0_length_2000/ALLEGRO/results/adsorption/config.yaml ADDED
@@ -0,0 +1,750 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ _jit_bailout_depth: 2
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+ _jit_fuser: fuser1
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+ _jit_fusion_strategy:
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+ - !!python/tuple
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+ - DYNAMIC
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+ - 3
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+ allow_tf32: true
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+ append: true
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+ ase_args:
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+ format: extxyz
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+ avg_num_neighbors: 34.28703787878788
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+ batch_size: 32
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+ chemical_symbols:
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+ - H
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+ - C
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+ - N
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+ - Zn
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+ - O
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+ code_commits: {}
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+ dataloader_num_workers: 0
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+ dataset: ase
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+ dataset_AtomicData_options:
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+ r_max: 5.0
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+ dataset_file_name: ../trainset.xyz
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+ dataset_key_mapping:
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+ force: forces
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+ dataset_seed: 456
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+ dataset_statistics_stride: 1
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+ default_dtype: float64
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+ device: cuda
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+ e3nn_version: 0.5.4
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+ early_stopping: null
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+ early_stopping_kwargs: null
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+ edge_eng_mlp_latent_dimensions:
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+ - 128
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+ edge_eng_mlp_nonlinearity: null
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+ ema_decay: 0.999
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+ ema_use_num_updates: true
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+ end_of_batch_callbacks: []
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+ end_of_epoch_callbacks: []
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+ end_of_train_callbacks: []
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+ env_embed_mlp_latent_dimensions: []
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+ env_embed_mlp_nonlinearity: null
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+ env_embed_multiplicity: 16
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+ equivariance_test: false
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+ exclude_keys: []
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+ final_callbacks: []
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+ gpu_oom_offload: false
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+ grad_anomaly_mode: false
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+ init_callbacks: []
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+ irreps_edge_sh: 1x0e+1x1o+1x2e
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+ l_max: 2
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+ latent_mlp_latent_dimensions:
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+ - 512
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+ - 512
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+ latent_mlp_nonlinearity: silu
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+ latent_resnet: true
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+ learning_rate: 0.005
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+ log_batch_freq: 100
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+ log_epoch_freq: 1
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+ loss_coeffs:
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+ forces:
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+ - 20
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+ total_energy:
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+ - 1
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+ lr_scheduler_kwargs: {}
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+ lr_scheduler_name: none
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+ max_epochs: 500
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+ max_gradient_norm: .inf
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+ metrics_components:
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+ - - forces
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+ - mae
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+ - PerSpecies: false
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+ report_per_component: false
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+ - - total_energy
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+ - mae
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+ - PerAtom: false
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+ - - forces
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+ - rmse
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+ - PerSpecies: false
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+ report_per_component: false
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+ - - total_energy
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+ - rmse
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+ - PerAtom: false
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+ - - total_energy
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+ - mae
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+ - PerAtom: true
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+ - - total_energy
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+ - rmse
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+ - PerAtom: true
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+ metrics_key: validation_loss
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+ model_builders:
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+ - allegro.model.Allegro
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+ - PerSpeciesRescale
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+ - StressForceOutput
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+ - RescaleEnergyEtc
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+ model_debug_mode: false
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+ model_dtype: float32
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+ n_train: 1500
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+ n_train_per_epoch: null
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+ n_val: 500
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+ nequip_version: 0.6.1
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+ nonscalars_include_parity: true
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+ num_layers: 2
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+ num_types: 5
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+ optimizer_kwargs:
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+ amsgrad: false
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+ betas: !!python/tuple
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+ - 0.9
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+ - 0.999
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+ capturable: false
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+ differentiable: false
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+ eps: 1.0e-08
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+ foreach: null
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+ fused: null
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+ maximize: false
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+ weight_decay: 0
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+ optimizer_name: Adam
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+ parity: o3_full
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+ r_max: 5.0
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+ report_init_validation: true
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+ root: ./results/
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+ run_name: adsorption
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+ save_checkpoint_freq: -1
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+ save_ema_checkpoint_freq: -1
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+ seed: 123
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+ shuffle: true
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+ start_of_epoch_callbacks: []
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+ tensorboard: false
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+ torch_version: !!python/object/new:torch.torch_version.TorchVersion
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+ - 2.5.1+cu124
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+ train_idcs: !!python/object/apply:torch._utils._rebuild_tensor_v2
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715
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721
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725
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726
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730
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731
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733
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734
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735
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736
+ wgcAAAAAAADDBwAAAAAAAMQHAAAAAAAAxQcAAAAAAADGBwAAAAAAAMcHAAAAAAAAyAcAAAAAAADJ
737
+ BwAAAAAAAMoHAAAAAAAAywcAAAAAAADMBwAAAAAAAM0HAAAAAAAAzgcAAAAAAADPBwAAAAAAAA==
738
+ - 1500
739
+ - !!python/tuple
740
+ - 500
741
+ - !!python/tuple
742
+ - 1
743
+ - false
744
+ - !!python/object/apply:collections.OrderedDict
745
+ - []
746
+ validation_batch_size: 5
747
+ var_num_neighbors: 5.595568287958044
748
+ verbose: INFO
749
+ wandb: false
750
+ warn_unused: true
ads_0_length_2000/ALLEGRO/results/adsorption/log ADDED
@@ -0,0 +1,148 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Torch device: cuda
2
+ Number of weights: 1406856
3
+ Number of trainable weights: 1406856
4
+ ! Starting training ...
5
+
6
+ validation
7
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
8
+ 0 100 727 1.09 705 0.917 1.22 31.1 31.1 0.0883 0.0884
9
+
10
+
11
+ Initialization # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
12
+ ! Initial Validation 0 6.512 0.005 1.06 1.15e+03 1.17e+03 0.898 1.21 39.5 39.8 0.112 0.113
13
+ Wall time: 6.512737072072923
14
+ ! Best model 0 1174.431
15
+
16
+ training
17
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
18
+ 1 47 56.9 0.996 37 0.869 1.17 7.02 7.12 0.0199 0.0202
19
+
20
+ validation
21
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
22
+ 1 100 25.1 0.986 5.4 0.879 1.16 2.72 2.72 0.00772 0.00773
23
+
24
+
25
+ Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
26
+ ! Train 1 38.096 0.005 1.01 2.02e+04 2.02e+04 0.878 1.18 110 167 0.312 0.474
27
+ ! Validation 1 38.096 0.005 0.977 2.71 22.3 0.864 1.16 1.66 1.93 0.00472 0.00547
28
+ Wall time: 38.096726526040584
29
+ ! Best model 1 22.253
30
+
31
+ training
32
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
33
+ 2 47 28.3 0.987 8.56 0.867 1.16 3.11 3.43 0.00884 0.00974
34
+
35
+ validation
36
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
37
+ 2 100 27 0.986 7.33 0.879 1.16 3.17 3.17 0.009 0.00901
38
+
39
+
40
+ Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
41
+ ! Train 2 67.110 0.005 0.989 72.2 92 0.867 1.16 7.6 9.97 0.0216 0.0283
42
+ ! Validation 2 67.110 0.005 0.977 3.38 22.9 0.864 1.16 1.88 2.15 0.00534 0.00612
43
+ Wall time: 67.11061549978331
44
+
45
+ training
46
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
47
+ 3 47 20.4 0.97 1.01 0.863 1.15 0.932 1.18 0.00265 0.00334
48
+
49
+ validation
50
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
51
+ 3 100 21 0.985 1.25 0.879 1.16 1.3 1.31 0.0037 0.00372
52
+
53
+
54
+ Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
55
+ ! Train 3 96.100 0.005 0.988 1.65 21.4 0.867 1.16 1.21 1.5 0.00343 0.00427
56
+ ! Validation 3 96.100 0.005 0.977 1.46 21 0.864 1.16 1.1 1.42 0.00313 0.00402
57
+ Wall time: 96.10013025207445
58
+ ! Best model 3 20.996
59
+
60
+ training
61
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
62
+ 4 47 20.9 0.969 1.5 0.862 1.15 1.2 1.43 0.00342 0.00407
63
+
64
+ validation
65
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
66
+ 4 100 21.4 0.984 1.68 0.879 1.16 1.51 1.52 0.00429 0.00431
67
+
68
+
69
+ Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
70
+ ! Train 4 125.114 0.005 0.987 1.13 20.9 0.867 1.16 1 1.25 0.00285 0.00354
71
+ ! Validation 4 125.114 0.005 0.976 1.36 20.9 0.863 1.16 1.07 1.36 0.00305 0.00388
72
+ Wall time: 125.11449338681996
73
+ ! Best model 4 20.874
74
+
75
+ training
76
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
77
+ 5 47 21 1 0.977 0.872 1.17 0.907 1.16 0.00258 0.00329
78
+
79
+ validation
80
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
81
+ 5 100 20.9 0.983 1.25 0.878 1.16 1.3 1.31 0.0037 0.00373
82
+
83
+
84
+ Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
85
+ ! Train 5 154.129 0.005 0.986 1.12 20.8 0.866 1.16 0.99 1.24 0.00281 0.00352
86
+ ! Validation 5 154.129 0.005 0.975 1.48 21 0.863 1.16 1.11 1.42 0.00315 0.00404
87
+ Wall time: 154.1295640738681
88
+
89
+ training
90
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
91
+ 6 47 21 0.998 0.993 0.869 1.17 0.909 1.17 0.00258 0.00332
92
+
93
+ validation
94
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
95
+ 6 100 20.7 0.982 1.09 0.878 1.16 1.21 1.22 0.00345 0.00347
96
+
97
+
98
+ Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
99
+ ! Train 6 183.121 0.005 0.985 1.11 20.8 0.866 1.16 0.992 1.23 0.00282 0.00351
100
+ ! Validation 6 183.121 0.005 0.974 1.54 21 0.862 1.16 1.13 1.45 0.00321 0.00413
101
+ Wall time: 183.1215311489068
102
+
103
+ training
104
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
105
+ 7 47 20.8 0.985 1.13 0.867 1.16 0.923 1.24 0.00262 0.00353
106
+
107
+ validation
108
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
109
+ 7 100 21 0.981 1.44 0.877 1.16 1.4 1.4 0.00397 0.00399
110
+
111
+
112
+ Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
113
+ ! Train 7 212.118 0.005 0.984 1.12 20.8 0.865 1.16 0.996 1.24 0.00283 0.00352
114
+ ! Validation 7 212.118 0.005 0.972 1.4 20.8 0.862 1.16 1.09 1.39 0.00309 0.00394
115
+ Wall time: 212.11811342090368
116
+ ! Best model 7 20.849
117
+
118
+ training
119
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
120
+ 8 47 20.6 0.968 1.22 0.859 1.15 1.07 1.3 0.00303 0.00368
121
+
122
+ validation
123
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
124
+ 8 100 21 0.979 1.45 0.877 1.16 1.4 1.41 0.00398 0.004
125
+
126
+
127
+ Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
128
+ ! Train 8 241.128 0.005 0.983 1.11 20.8 0.865 1.16 0.99 1.23 0.00281 0.0035
129
+ ! Validation 8 241.128 0.005 0.971 1.39 20.8 0.861 1.15 1.08 1.38 0.00308 0.00393
130
+ Wall time: 241.12826776411384
131
+ ! Best model 8 20.812
132
+
133
+ training
134
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
135
+ 9 47 20.6 0.972 1.14 0.862 1.15 1.08 1.25 0.00306 0.00355
136
+
137
+ validation
138
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
139
+ 9 100 20.6 0.978 1.08 0.876 1.16 1.21 1.22 0.00344 0.00346
140
+
141
+
142
+ Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
143
+ ! Train 9 270.190 0.005 0.981 1.1 20.7 0.864 1.16 0.986 1.23 0.0028 0.00349
144
+ ! Validation 9 270.190 0.005 0.97 1.52 20.9 0.861 1.15 1.12 1.44 0.00319 0.0041
145
+ Wall time: 270.190540751908
146
+
147
+ training
148
+ # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse
ads_0_length_2000/ALLEGRO/results/adsorption/metrics_batch_train.csv ADDED
@@ -0,0 +1,446 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ epoch, batch, loss, loss_f, loss_e, f_mae, f_rmse, e_mae, e_rmse, e/N_mae, e/N_rmse
2
+ 1, 1, 1346.8, 1.0706, 1325.4, 0.89721, 1.2121, 42.304, 42.648, 0.12018, 0.12116
3
+ 1, 2, 3.1392e+05, 1.1277, 3.139e+05, 0.94641, 1.244, 655.99, 656.32, 1.8636, 1.8646
4
+ 1, 3, 22190, 1.0104, 22170, 0.88195, 1.1775, 174.36, 174.42, 0.49534, 0.49552
5
+ 1, 4, 50030, 1.1179, 50007, 0.92318, 1.2386, 261.54, 261.96, 0.74302, 0.74421
6
+ 1, 5, 1.3907e+05, 1.2096, 1.3905e+05, 0.962, 1.2884, 436.25, 436.82, 1.2393, 1.241
7
+ 1, 6, 1.1089e+05, 1.1604, 1.1087e+05, 0.93907, 1.2619, 389.57, 390.05, 1.1067, 1.1081
8
+ 1, 7, 42501, 1.0923, 42479, 0.91226, 1.2243, 241.07, 241.44, 0.68484, 0.68591
9
+ 1, 8, 3932.1, 1.0491, 3911.1, 0.88882, 1.1999, 73.128, 73.261, 0.20775, 0.20813
10
+ 1, 9, 4145.4, 1, 4125.4, 0.8717, 1.1715, 75.154, 75.241, 0.21351, 0.21375
11
+ 1, 10, 23604, 0.98641, 23584, 0.87111, 1.1635, 179.7, 179.9, 0.51052, 0.51108
12
+ 1, 11, 39969, 0.99505, 39949, 0.8773, 1.1685, 233.86, 234.14, 0.66438, 0.66517
13
+ 1, 12, 42087, 0.9824, 42067, 0.86839, 1.1611, 240.02, 240.27, 0.68189, 0.68258
14
+ 1, 13, 33732, 0.98857, 33712, 0.87197, 1.1647, 214.83, 215.09, 0.61032, 0.61105
15
+ 1, 14, 19263, 0.9961, 19243, 0.87231, 1.1692, 162.27, 162.5, 0.46099, 0.46166
16
+ 1, 15, 6777.3, 0.97688, 6757.8, 0.86283, 1.1578, 96.157, 96.3, 0.27317, 0.27358
17
+ 1, 16, 589.72, 1.0047, 569.62, 0.87104, 1.1742, 27.895, 27.959, 0.079248, 0.079428
18
+ 1, 17, 872.47, 0.99462, 852.58, 0.86513, 1.1683, 34.139, 34.205, 0.096985, 0.097173
19
+ 1, 18, 5329, 1.0055, 5308.9, 0.8707, 1.1747, 85.203, 85.354, 0.24205, 0.24248
20
+ 1, 19, 10385, 1.0155, 10365, 0.87824, 1.1805, 119.11, 119.26, 0.33837, 0.33882
21
+ 1, 20, 13419, 1.0066, 13399, 0.87552, 1.1753, 135.37, 135.6, 0.38457, 0.38523
22
+ 1, 21, 13872, 1.0229, 13852, 0.87645, 1.1848, 137.7, 137.87, 0.39118, 0.39168
23
+ 1, 22, 11290, 1.0161, 11270, 0.87844, 1.1808, 124.18, 124.36, 0.35278, 0.35329
24
+ 1, 23, 7280.1, 0.99675, 7260.1, 0.86802, 1.1695, 99.656, 99.815, 0.28311, 0.28357
25
+ 1, 24, 3416.2, 0.99292, 3396.3, 0.8697, 1.1673, 68.141, 68.27, 0.19358, 0.19395
26
+ 1, 25, 906.84, 1.007, 886.7, 0.87487, 1.1755, 34.825, 34.883, 0.098934, 0.099099
27
+ 1, 26, 28.861, 0.96916, 9.478, 0.86146, 1.1532, 3.2817, 3.6065, 0.009323, 0.010246
28
+ 1, 27, 506.02, 0.99455, 486.13, 0.86456, 1.1682, 25.745, 25.829, 0.07314, 0.073377
29
+ 1, 28, 1741.1, 0.97033, 1721.7, 0.85931, 1.1539, 48.495, 48.607, 0.13777, 0.13809
30
+ 1, 29, 3327.8, 1.005, 3307.7, 0.87154, 1.1744, 67.262, 67.373, 0.19109, 0.1914
31
+ 1, 30, 4124.3, 0.98253, 4104.7, 0.8652, 1.1612, 74.918, 75.052, 0.21284, 0.21322
32
+ 1, 31, 4250.9, 0.97626, 4231.3, 0.86488, 1.1575, 76.036, 76.201, 0.21601, 0.21648
33
+ 1, 32, 3741.8, 0.97975, 3722.2, 0.86169, 1.1595, 71.311, 71.47, 0.20259, 0.20304
34
+ 1, 33, 2612.3, 0.99211, 2592.5, 0.86984, 1.1668, 59.482, 59.646, 0.16898, 0.16945
35
+ 1, 34, 1641.9, 0.97393, 1622.5, 0.86473, 1.1561, 47.061, 47.186, 0.1337, 0.13405
36
+ 1, 35, 726.24, 0.97489, 706.74, 0.86048, 1.1566, 31.059, 31.142, 0.088235, 0.088473
37
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