model: precision: 64 num_chem_elements: 4 edge_cutoff: 4.0 num_edge_rbf: 8 num_edge_poly_cutoff: 6 num_vel_rbf: 8 vel_max: 0.11 max_rotation_order: 2 num_hidden_channels: 64 num_mp_layers: 4 edge_mlp_kwargs: n_neurons: - 64 - 64 - 64 activation: silu vel_mlp_kwargs: n_neurons: - 64 - 64 - 64 activation: silu nl_gate_kwargs: activation_scalars: o: tanh e: silu activation_gates: e: silu conserve_ang_mom: true o3_backend: cueq net_lin_mom: - 0.0 - 0.0 - 0.0 net_ang_mom: - 0.0 - 0.0 - 0.0 data: root: /dccstor/chemistry_ai/trajcast/paper/paracetamol/data name: paracetamol_300K_N10000_dt7.0_rc4.0_train cutoff_radius: 4.0 files: - paracetamol_precision_300K_rep1_timestep70_train3334.extxyz - paracetamol_precision_300K_rep2_timestep70_train3333.extxyz - paracetamol_precision_300K_rep3_timestep70_train3333.extxyz rename: true atom_type_mapper: 1: 0 6: 1 7: 2 8: 3 training: seed: 2303 model_type: EfficientTrajCastModel device: cuda restart_latest: true target_field: target reference_fields: - displacements - update_velocities batch_size: 10 max_grad_norm: 0.5 num_epochs: 1500 criterion: loss_type: main_loss: mse learnable_weights: false optimizer: adam optimizer_settings: lr: 0.01 amsgrad: true scheduler: - ReduceLROnPlateau scheduler_settings: ReduceLROnPlateau: factor: 0.8 patience: 25 min_lr: 0.0001 chained_scheduler_hp: milestones: - 100000000 per_epoch: true monitor_lr_scheduler: false tensorboard_settings: loss: true lr: true loss_validation: data: root: /dccstor/chemistry_ai/trajcast/paper/paracetamol/data name: paracetamol_300K_N10000_dt7.0_rc4.0_val files: - paracetamol_precision_300K_rep4_timestep70_val2500.extxyz cutoff_radius: 4.0 rename: true atom_type_mapper: 1: 0 6: 1 7: 2 8: 3