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Trig-ODE-Gamma/checkpoint.ckpt +3 -0
Trig-ODE-Gamma/train.yaml +149 -0
Trig-ODE/checkpoint.ckpt +3 -0
Trig-ODE/train.yaml +152 -0
Trig-SDE-Gamma/checkpoint.ckpt +3 -0
Trig-SDE-Gamma/train.yaml +146 -0
VESBD-ODE/checkpoint.ckpt +3 -0
VESBD-ODE/train.yaml +156 -0
VPSBD-ODE/checkpoint.ckpt +3 -0
VPSBD-ODE/train.yaml +139 -0
VPSBD-SDE/checkpoint.ckpt +3 -0
VPSBD-SDE/train.yaml +149 -0

Trig-ODE-Gamma/checkpoint.ckpt ADDED Viewed

	@@ -0,0 +1,3 @@

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+size 148099774

Trig-ODE-Gamma/train.yaml ADDED Viewed

	@@ -0,0 +1,149 @@

+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.PeriodicTrigonometricInterpolant
+            gamma:
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 0.03337798944475465
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 13.545929738762764
+            correct_center_of_mass_motion: true
+        # lattice vectors
+        - class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.interpolants.LinearInterpolant
+            gamma:
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 0.017261010545698854
+            epsilon:
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 0.8758328635983847
+                mu: 0.29744423858325936
+                sigma: 0.0052236060273636595
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.0012811297783628106
+            velocity_annealing_factor: 2.380421528846764
+            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: 780
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.983015308902659
+    cell_loss_b: 0.01673796318800159
+    cell_loss_z: 0.0002467279093394523
+  sampler:
+    class_path: omg.sampler.sample_from_rng.SampleFromRNG
+    init_args:
+      pos_distribution: null
+      cell_distribution:
+        class_path: omg.sampler.distributions.InformedLatticeDistribution
+        init_args:
+          dataset_name: alex_mp_20
+      species_distribution:
+        class_path: omg.sampler.distributions.MirrorData
+  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"
+  number_cpus: 7
+  dataset_name: "alex_mp_20"
+data:
+  train_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/train.lmdb"
+      niggli: True
+  val_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/val.lmdb"
+      niggli: True
+  predict_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/test.lmdb"
+      niggli: True
+  batch_size: 32
+  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: 2000
+  enable_progress_bar: true
+  limit_val_batches: 0.1
+  check_val_every_n_epoch: 100
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 8.341737878937152e-05

Trig-ODE/checkpoint.ckpt ADDED Viewed

	@@ -0,0 +1,3 @@

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+oid sha256:047eabb29f750a4f9d49d088d3f1e7cb1231ba58f3c5456d63859453fb347ff4
+size 148100284

Trig-ODE/train.yaml ADDED Viewed

	@@ -0,0 +1,152 @@

+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.PeriodicTrigonometricInterpolant
+            gamma: null
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 12.34532470785473
+            correct_center_of_mass_motion: true
+        # lattice vectors
+        - class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
+          init_args:
+            interpolant:
+              class_path: omg.si.interpolants.EncoderDecoderInterpolant
+              init_args:
+                switch_time: 0.4080329374611481
+                power: 0.5
+            gamma:
+              class_path: omg.si.gamma.LatentGammaEncoderDecoder
+              init_args:
+                a: 5.270616141661882
+                switch_time: 0.4080329374611481
+                power: 0.5
+            epsilon:
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 4.354817546796119
+                mu: 0.2923928859901851
+                sigma: 0.04742031136770322
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.005905325524508953
+            velocity_annealing_factor: 3.6141717997883447
+            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: 170
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.9967455480681945
+    cell_loss_b: 0.002271914623580616
+    cell_loss_z: 0.0009825373082248405
+  sampler:
+    class_path: omg.sampler.sample_from_rng.SampleFromRNG
+    init_args:
+      pos_distribution: null
+      cell_distribution:
+        class_path: omg.sampler.distributions.InformedLatticeDistribution
+        init_args:
+          dataset_name: alex_mp_20
+      species_distribution:
+        class_path: omg.sampler.distributions.MirrorData
+  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"
+  number_cpus: 7
+  dataset_name: "alex_mp_20"
+data:
+  train_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/train.lmdb"
+      niggli: False
+  val_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/val.lmdb"
+      niggli: False
+  predict_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/test.lmdb"
+      niggli: False
+  batch_size: 32
+  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: 2000
+  enable_progress_bar: true
+  limit_val_batches: 0.1
+  check_val_every_n_epoch: 100
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 3.629490873183724e-05

Trig-SDE-Gamma/checkpoint.ckpt ADDED Viewed

	@@ -0,0 +1,3 @@

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+size 148075198

Trig-SDE-Gamma/train.yaml ADDED Viewed

	@@ -0,0 +1,146 @@

+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.PeriodicTrigonometricInterpolant
+            gamma:
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 0.049242906264339095
+            epsilon:
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 9.418703639528207
+                mu: 0.1967838464371502
+                sigma: 0.040028404066547216
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.0013504737289622426
+            velocity_annealing_factor: 11.483173553510193
+            correct_center_of_mass_motion: true
+        # lattice vectors
+        - class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.interpolants.TrigonometricInterpolant
+            gamma: null
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 0.4337356395028541
+            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: 740
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.24677273761024368
+    pos_loss_z: 0.7231540118244248
+    cell_loss_b: 0.030073250565331323
+  sampler:
+    class_path: omg.sampler.sample_from_rng.SampleFromRNG
+    init_args:
+      pos_distribution: null
+      cell_distribution:
+        class_path: omg.sampler.distributions.InformedLatticeDistribution
+        init_args:
+          dataset_name: alex_mp_20
+      species_distribution:
+        class_path: omg.sampler.distributions.MirrorData
+  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: True
+  float_32_matmul_precision: "high"
+  validation_mode: "match_rate"
+  number_cpus: 7
+  dataset_name: "alex_mp_20"
+data:
+  train_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/train.lmdb"
+      niggli: True
+  val_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/val.lmdb"
+      niggli: True
+  predict_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/test.lmdb"
+      niggli: True
+  batch_size: 32
+  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: 2000
+  enable_progress_bar: true
+  limit_val_batches: 0.1
+  check_val_every_n_epoch: 100
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 9.320780466656964e-05

VESBD-ODE/checkpoint.ckpt ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:a4442b50d380f7df53088cbc48ebb683ad5f768bf89dda93fa695ea1f339e289
+size 148100602

VESBD-ODE/train.yaml ADDED Viewed

	@@ -0,0 +1,156 @@

+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_os.SingleStochasticInterpolantOS
+          init_args:
+            interpolant:
+              class_path: omg.si.interpolants.PeriodicScoreBasedDiffusionModelInterpolantVE
+              init_args:
+                sigma:
+                  class_path: omg.si.sigma.GeometricSigma
+                  init_args:
+                    sigma_min: 0.004705415831077799
+                    sigma_max: 0.9967130801483843
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 8.284579088906593
+            correct_center_of_mass_motion: true
+            predict_velocity: true
+        # lattice vectors
+        - class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.interpolants.LinearInterpolant
+            gamma:
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 0.016616684357970132
+            epsilon:
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 3.9372558236242052
+                mu: 0.2649556265396099
+                sigma: 0.03578203230805775
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.0015144158387556672
+            velocity_annealing_factor: 0.42775377056075214
+            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: 660
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.9813067351598369
+    cell_loss_b: 0.0005256953168558359
+    cell_loss_z: 0.018167569523307267
+  sampler:
+    class_path: omg.sampler.sample_from_rng.SampleFromRNG
+    init_args:
+      pos_distribution:
+        class_path: omg.sampler.distributions.NormalDistribution
+        init_args:
+          scale: 9.77149759679434
+      cell_distribution:
+        class_path: omg.sampler.distributions.InformedLatticeDistribution
+        init_args:
+          dataset_name: alex_mp_20
+      species_distribution:
+        class_path: omg.sampler.distributions.MirrorData
+  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"
+  number_cpus: 7
+  dataset_name: "alex_mp_20"
+data:
+  train_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/train.lmdb"
+      niggli: False
+  val_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/val.lmdb"
+      niggli: False
+  predict_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/test.lmdb"
+      niggli: 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: 2000
+  enable_progress_bar: true
+  limit_val_batches: 0.1
+  check_val_every_n_epoch: 100
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 0.000296636127734534

VPSBD-ODE/checkpoint.ckpt ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:f041997c8526a5e3162958f0196584f6ae086f2a09f32774b5bea7465dcc2a76
+size 148062466

VPSBD-ODE/train.yaml ADDED Viewed

	@@ -0,0 +1,139 @@

+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_os.SingleStochasticInterpolantOS
+          init_args:
+            interpolant: omg.si.interpolants.PeriodicScoreBasedDiffusionModelInterpolant
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 6.613808424917352
+            correct_center_of_mass_motion: true
+            predict_velocity: 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: 2.447993013544224
+            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: 890
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.9597565150933746
+    cell_loss_b: 0.04024348490662539
+  sampler:
+    class_path: omg.sampler.sample_from_rng.SampleFromRNG
+    init_args:
+      pos_distribution:
+        class_path: omg.sampler.distributions.NormalDistribution
+        init_args:
+          scale: 0.22006712732536396
+      cell_distribution:
+        class_path: omg.sampler.distributions.InformedLatticeDistribution
+        init_args:
+          dataset_name: alex_mp_20
+      species_distribution:
+        class_path: omg.sampler.distributions.MirrorData
+  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: True
+  float_32_matmul_precision: "high"
+  validation_mode: "match_rate"
+  number_cpus: 7
+  dataset_name: "alex_mp_20"
+data:
+  train_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/train.lmdb"
+      niggli: True
+  val_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/val.lmdb"
+      niggli: True
+  predict_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/test.lmdb"
+      niggli: True
+  batch_size: 64
+  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: 2000
+  enable_progress_bar: true
+  limit_val_batches: 0.1
+  check_val_every_n_epoch: 100
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 2.519765029616902e-05

VPSBD-SDE/checkpoint.ckpt ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:07cee771817165d46f06d84f3593e3b9f01ef3a8f782de027a70c411ca88bfc1
+size 49644411

VPSBD-SDE/train.yaml ADDED Viewed

	@@ -0,0 +1,149 @@

+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_os.SingleStochasticInterpolantOS
+          init_args:
+            interpolant: omg.si.interpolants.PeriodicScoreBasedDiffusionModelInterpolant
+            epsilon:
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 2.4729222108905815
+                mu: 0.17656358406313838
+                sigma: 0.02379822283154629
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.0016661101253703237
+            velocity_annealing_factor: 6.459028320375323
+            correct_center_of_mass_motion: true
+            predict_velocity: true
+        # lattice vectors
+        - class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.interpolants.LinearInterpolant
+            gamma:
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 3.683542379054881
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 0.6692350794589719
+            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: 600
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.6060249654155797
+    pos_loss_z: 0.3828230559814603
+    cell_loss_b: 0.011151978602959979
+  sampler:
+    class_path: omg.sampler.sample_from_rng.SampleFromRNG
+    init_args:
+      pos_distribution:
+        class_path: omg.sampler.distributions.NormalDistribution
+        init_args:
+          scale: 2.2937003279036148
+      cell_distribution:
+        class_path: omg.sampler.distributions.InformedLatticeDistribution
+        init_args:
+          dataset_name: alex_mp_20
+      species_distribution:
+        class_path: omg.sampler.distributions.MirrorData
+  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: True
+  float_32_matmul_precision: "high"
+  validation_mode: "match_rate"
+  number_cpus: 7
+  dataset_name: "alex_mp_20"
+data:
+  train_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/train.lmdb"
+      niggli: True
+  val_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/val.lmdb"
+      niggli: True
+  predict_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/alex_mp_20/test.lmdb"
+      niggli: True
+  batch_size: 64
+  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: 2000
+  enable_progress_bar: true
+  limit_val_batches: 0.1
+  check_val_every_n_epoch: 100
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 0.0003030820420973639