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model:
si:
class_path: omg.si.stochastic_interpolants.StochasticInterpolants
init_args:
stochastic_interpolants:
# chemical species
- class_path: omg.si.single_stochastic_interpolant_identity.SingleStochasticInterpolantIdentity
# fractional coordinates
- class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
init_args:
interpolant: omg.si.interpolants.PeriodicLinearInterpolant
gamma: null
epsilon: null
differential_equation_type: "ODE"
integrator_kwargs:
method: "euler"
velocity_annealing_factor: 12.752963137656907
correct_center_of_mass_motion: true
# lattice vectors
- class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
init_args:
interpolant: omg.si.interpolants.LinearInterpolant
gamma: null
epsilon: null
differential_equation_type: "ODE"
integrator_kwargs:
method: "euler"
velocity_annealing_factor: 0.9964121490291458
correct_center_of_mass_motion: false
data_fields:
# if the order of the data_fields changes,
# the order of the above StochasticInterpolant inputs must also change
- "species"
- "pos"
- "cell"
integration_time_steps: 100
relative_si_costs:
species_loss: 0.0
pos_loss_b: 0.9983149306572928
cell_loss_b: 0.0016850693427072152
sampler:
class_path: omg.sampler.IndependentSampler
init_args:
pos_distribution:
class_path: omg.sampler.position_distributions.UniformPositionDistribution
cell_distribution:
class_path: omg.sampler.cell_distributions.InformedLatticeDistribution
init_args:
dataset_name: mpts_52
species_distribution:
class_path: omg.sampler.species_distributions.MirrorSpecies
model:
class_path: omg.model.model.Model
init_args:
encoder:
class_path: omg.model.encoders.cspnet_full.CSPNetFull
head:
class_path: omg.model.heads.pass_through.PassThrough
time_embedder:
class_path: omg.model.model_utils.SinusoidalTimeEmbeddings
init_args:
dim: 256
use_min_perm_dist: False
float_32_matmul_precision: "high"
validation_mode: "match_rate"
dataset_name: "mpts_52"
data:
train_dataset:
class_path: omg.datamodule.StructureDataset
init_args:
file_path: "data/mpts_52/train.lmdb"
lazy_storage: True
niggli_reduce: False
val_dataset:
class_path: omg.datamodule.StructureDataset
init_args:
file_path: "data/mpts_52/val.lmdb"
lazy_storage: True
niggli_reduce: False
pred_dataset:
class_path: omg.datamodule.StructureDataset
init_args:
file_path: "data/mpts_52/test.lmdb"
lazy_storage: True
niggli_reduce: False
batch_size: 512
num_workers: 4
pin_memory: True
persistent_workers: True
trainer:
callbacks:
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
filename: "best_val_loss_total"
save_top_k: 1
monitor: "val_loss_total"
save_weights_only: true
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
filename: "best_val_match_rate"
save_top_k: 1
monitor: "match_rate"
save_weights_only: true
mode: 'max'
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
filename: "best_val_rmsd"
save_top_k: 1
monitor: "mean_rmsd"
save_weights_only: true
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
save_top_k: -1 # Store every checkpoint after 100 epochs.
monitor: "val_loss_total"
every_n_epochs: 100
save_weights_only: false
gradient_clip_val: 0.5
num_sanity_val_steps: 0
precision: "32-true"
max_epochs: 10000
enable_progress_bar: false
check_val_every_n_epoch: 100
optimizer:
class_path: torch.optim.Adam
init_args:
lr: 0.0005546288717347031
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