
I have run this code using the single-chain CG polymer dataset and encountered some issues.
- It seems that the default hyperparameters in graphwm/conf/model/chain.yaml do not use PnR for the single-chain CG polymer dataset. I have found that if I want to use PnR for this dataset, I should add score_gn_hparams: {'latent_dim': 128, 'units': 128, 'layers': 2, 'mp_steps': 7}, sigma_begin, sigma_end, sigma_level, and anneal_power in graphwm/conf/model/chain.yaml, but what should I set these parameters to? Does PnR perform better than GNS in the single-chain CG polymer dataset?
- I have run this code using the GNS model and the single-chain CG polymer dataset with the default hyperparameters in train.yaml and eval.yaml. I then got a .pt file with the keys: ['cg_u_pos', 'cg_weights', 'rollout_u_pos', 'rollout_prop', 'bonds', 'n_bond', 'position', 'rgs', 'target', 'target_rgs', 'particle_types', 'n_particle', 'keypoint', 'n_keypoint', 'cluster', 'cg_bonds', 'n_cg_bond', 'time_elapsed', 'model_params', 'eval_cfg']. 'rollout_prop' is what I need; it stores the rgs of each step. I saved this data in a .txt file. The true rgs is stored in a .h5 file in polymer_test_5M. I can calculate the MAE with the predicted results and true results, but I find it difficult to achieve the MAE result of 1.4 mentioned in the paper. I think I might have made a mistake somewhere; how should I calculate the MAE?
Any suggestions would be very helpful to me; thank you very much.
I have run this code using the single-chain CG polymer dataset and encountered some issues.
Any suggestions would be very helpful to me; thank you very much.