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BAM_MPtrj_v1

PyTorch
bam-torch
machine-learning-potential
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BAM_MPtrj_v1
File size: 2,443 Bytes
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{
    "device": "gpu",
    "model": "race",
    "gpu-parallel": true,
    "trainer": "base",
    "fname_traj": "/dataset/usr004/hgpark/mptrj/pkl/train_edges_0712_76800",
    "ntrain": "/dataset/usr004/hgpark/mptrj/pkl/train_edges_0712_76800/",
    "nvalid": "/dataset/usr004/hgpark/mptrj/pkl/valid_edges_0712_7680/",
    "element": "auto",
    "enr_avg_per_element": "get_enr_avg_per_element.txt",
    "cutoff": 6.0,
    "avg_num_neighbors": 60,
    "num_species": 89,
    "hidden_channels": "128x0e + 64x1o + 32x2e + 32x3o",
    "features_dim": 64,
    "num_radial_basis": 8,
    "nlayers": 5,
    "max_ell": 3,
    "output_channels": "1x0e",
    "active_fn": "identity",
    "regress_forces": true,
    "cueq_config": false,
    "nbatch": 1,
    "dataloader": {
        "num_workers": 0,
        "persistent_workers": false,
        "prefetch_factor": 2
    },
    "pbc": true,
    "NN": {
        "data_seed": 1300,
        "init_seed": 1301,
        "learning_rate": 0.01,
        "weight_decay": 0,
        "nepoch": 100,
        "nsave": 1,
        "restart": true,
        "fname_pkl": "/dataset/usr004/hgpark/mptrj/test-bias-260507/model.pkl",
        "loss_config": {
            "huber_loss": "huber",
            "energy_loss": "huber",
            "force_loss": "huber",
            "stress_loss": "huber",
            "huber_delta": 0.01
        },
        "enr_lambda": 10,
        "frc_lambda": 1,
        "str_lambda": 10,
        "energy_grad_loss": true,
        "energy_grad_mult": 10,
        "l2_lambda": 0.0,
        "cosine_sim": false
    },
    "scheduler": {
        "scheduler": "ReduceLROnPlateau",
        "decay_factor": 0.7,
        "patience": 2,
        "threshold": 0.01,
        "min_lr": 1e-06,
        "cooldown": 1
    },
    "log_length": "simple",
    "log_interval": 2,
    "log_config": {
        "step": [
            "date",
            "epoch"
        ],
        "train": [
            "loss",
            "loss_e",
            "loss_f",
            "loss_s"
        ],
        "valid": [
            "loss",
            "loss_e",
            "loss_f",
            "loss_s"
        ],
        "lr": [
            "lr"
        ]
    },
    "train": {
        "fname_log": "loss_train.out"
    },
    "predict": {
        "evaluate_tag": false,
        "fname_traj": "../data/test_data",
        "ndata": "../data/test_data",
        "model": "model.pkl",
        "fname_plog": "predict.out"
    }
}