base_model/config.json

{
  "model_type": "djmgnn",
  "architecture": "Dense Jump Multi-Graph Neural Network",
  "task": "molecular_property_prediction",
  "framework": "pytorch",
  "library_name": "moml",
  "hidden_dim": 128,
  "n_blocks": 3,
  "layers_per_block": 6,
  "in_node_dim": 11,
  "in_edge_dim": 0,
  "node_output_dims": 3,
  "graph_output_dims": 19,
  "energy_output_dims": 1,
  "jk_mode": "cat",
  "dropout": 0.2,
  "use_supernode": true,
  "use_rbf": true,
  "rbf_K": 32,
  "training": {
    "epochs": 100,
    "batch_size": 32,
    "learning_rate": 0.001,
    "optimizer": "Adam",
    "early_stopping": true,
    "patience": 10,
    "validation_split": 0.2
  },
  "checkpoint_path": "checkpoints/checkpoint_step_20.pt",
  "model_name": "djmgnn-base",
  "license": "mit",
  "tags": [
    "molecular-property-prediction",
    "graph-neural-network",
    "chemistry",
    "pytorch"
  ],
  "datasets": [
    "qm9",
    "spice",
    "pfas"
  ],
  "metrics": [
    "mse",
    "mae"
  ],
  "pipeline_tag": "graph-ml"
}