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# Application of 7net-0 on liquid electrolyte system via fine-tuning
# Paper: https://arxiv.org/abs/2501.05211

model:
    # parameters of SevenNet-0, should not be changed
    chemical_species: 'auto'
    cutoff: 5.0
    channel: 128
    is_parity: False
    lmax: 2
    num_convolution_layer: 5
    irreps_manual:
        - "128x0e"
        - "128x0e+64x1e+32x2e"
        - "128x0e+64x1e+32x2e"
        - "128x0e+64x1e+32x2e"
        - "128x0e+64x1e+32x2e"
        - "128x0e"
    weight_nn_hidden_neurons: [64, 64]
    radial_basis:
        radial_basis_name: 'bessel'
        bessel_basis_num: 8
    cutoff_function:
        cutoff_function_name: 'XPLOR'
        cutoff_on: 4.5

    act_gate: {'e': 'silu', 'o': 'tanh'}
    act_scalar: {'e': 'silu', 'o': 'tanh'}

    self_connection_type: 'linear'

    # useful for fine-tuning
    train_shift_scale: True
    train_avg_num_neigh: True

train:
    random_seed: 1
    is_train_stress: True
    epoch: 100  # we went through 100 epochs and chose checkpoint at 50 epoch where the error have reached plateau.

    loss: 'Huber'
    loss_param:
        delta: 0.01

    optimizer: 'adam'
    optim_param:
        lr: 0.0001
    scheduler: 'linearlr'
    scheduler_param:
        start_factor: 1.0
        total_iters: 600
        end_factor: 0.000001

    force_loss_weight: 1.00
    stress_loss_weight: 1.00  # 7net-0 quantitatively lacked accuracy on pressure histograms compared to DFT, so we increased stress loss weight

    error_record:
        - ['Energy', 'RMSE']
        - ['Force', 'RMSE']
        - ['Stress', 'RMSE']
        - ['Energy', 'MAE']
        - ['Force', 'MAE']
        - ['Stress', 'MAE']
        - ['Energy', 'Loss']
        - ['Force', 'Loss']
        - ['Stress', 'Loss']
        - ['TotalLoss', 'None']

    per_epoch: 10   # Generate epoch every this number of times

    continue:
        use_statistic_values_of_checkpoint: True
        checkpoint: '7net-0'  # fine-tuning from 7net-0
        reset_optimizer: True
        reset_scheduler: True

data:
    batch_size: 1   # our fine-tuning dataset had ~360 atoms per structure, so we used batch size of 1 to avoid GPU OOM error.
    shift: 'elemwise_reference_energies'
    scale: 1.858
    data_format: 'ase'
    data_divide_ratio: 0.05
    load_dataset_path: ["./data/total.extxyz"]