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data:
root_path: ${oc.env:PROJECT_ROOT}/data/alex_20
prop: eah
num_targets: 1
niggli: true
primitive: false
graph_method: crystalnn
lattice_scale_method: scale_length
preprocess_workers: 30
readout: mean
max_atoms: 20
otf_graph: false
eval_model_name: alex20
tolerance: 0.1
use_space_group: false
use_pos_index: false
train_max_epochs: 6000
early_stopping_patience: 60
teacher_forcing_max_epoch: 500
datamodule:
_target_: diffcsp.pl_data.datamodule.CrystDataModule
datasets:
train:
_target_: diffcsp.pl_data.dataset.CrystDataset
name: Formation energy train
path: ${data.root_path}/train.csv
save_path: ${data.root_path}/train_ori.pt
prop: ${data.prop}
niggli: ${data.niggli}
primitive: ${data.primitive}
graph_method: ${data.graph_method}
tolerance: ${data.tolerance}
use_space_group: ${data.use_space_group}
use_pos_index: ${data.use_pos_index}
lattice_scale_method: ${data.lattice_scale_method}
preprocess_workers: ${data.preprocess_workers}
val:
- _target_: diffcsp.pl_data.dataset.CrystDataset
name: Formation energy val
path: ${data.root_path}/val.csv
save_path: ${data.root_path}/val_ori.pt
prop: ${data.prop}
niggli: ${data.niggli}
primitive: ${data.primitive}
graph_method: ${data.graph_method}
tolerance: ${data.tolerance}
use_space_group: ${data.use_space_group}
use_pos_index: ${data.use_pos_index}
lattice_scale_method: ${data.lattice_scale_method}
preprocess_workers: ${data.preprocess_workers}
test:
- _target_: diffcsp.pl_data.dataset.CrystDataset
name: Formation energy test
path: ${data.root_path}/test.csv
save_path: ${data.root_path}/test_ori.pt
prop: ${data.prop}
niggli: ${data.niggli}
primitive: ${data.primitive}
graph_method: ${data.graph_method}
tolerance: ${data.tolerance}
use_space_group: ${data.use_space_group}
use_pos_index: ${data.use_pos_index}
lattice_scale_method: ${data.lattice_scale_method}
preprocess_workers: ${data.preprocess_workers}
num_workers:
train: 0
val: 0
test: 0
batch_size:
train: 4096
val: 4096
test: 1024
logging:
val_check_interval: 1
wandb:
name: ${expname}
project: diffcsp
entity: null
log_model: true
mode: online
group: ${expname}
wandb_watch:
log: all
log_freq: 500
lr_monitor:
logging_interval: step
log_momentum: false
model:
decoder:
_target_: diffcsp.pl_modules.cspnet.CSPNet
hidden_dim: 512
latent_dim: 0
max_atoms: 100
num_layers: 6
act_fn: silu
dis_emb: sin
num_freqs: 128
edge_style: fc
max_neighbors: ${model.max_neighbors}
cutoff: ${model.radius}
ln: true
ip: true
beta_scheduler:
_target_: diffcsp.pl_modules.diff_utils.BetaScheduler
timesteps: ${model.timesteps}
scheduler_mode: cosine
sigma_scheduler:
_target_: diffcsp.pl_modules.diff_utils.SigmaScheduler
timesteps: ${model.timesteps}
sigma_begin: 0.005
sigma_end: 0.5
_target_: diffcsp.pl_modules.diffusion_w_type.CSPDiffusion
time_dim: 256
latent_dim: 0
cost_coord: 1.0
cost_lattice: 1.0
cost_type: 20.0
max_neighbors: 20
radius: 7.0
timesteps: 1000
optim:
optimizer:
_target_: torch.optim.Adam
lr: 0.001
betas:
- 0.9
- 0.999
eps: 1.0e-08
weight_decay: 0
use_lr_scheduler: true
lr_scheduler:
_target_: torch.optim.lr_scheduler.ReduceLROnPlateau
factor: 0.6
patience: 30
min_lr: 0.0001
train:
deterministic: true
random_seed: 42
pl_trainer:
fast_dev_run: false
accelerator: auto
precision: 32
max_epochs: ${data.train_max_epochs}
accumulate_grad_batches: 1
num_sanity_val_steps: 2
gradient_clip_val: 0.5
gradient_clip_algorithm: value
profiler: simple
monitor_metric: val_loss
monitor_metric_mode: min
early_stopping:
patience: ${data.early_stopping_patience}
verbose: true
model_checkpoints:
save_top_k: 1
verbose: false
save_last: false
ckpt_path: null
expname: alex_20
core:
version: 0.0.1
tags:
- ${now:%Y-%m-%d}