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EncDec-ODE-Gamma/checkpoint.ckpt +3 -0
EncDec-ODE-Gamma/train.yaml +155 -0
EncDec-SDE-Gamma/checkpoint.ckpt +3 -0
EncDec-SDE-Gamma/train.yaml +155 -0
Linear-ODE-Gamma/checkpoint.ckpt +3 -0
Linear-ODE-Gamma/train.yaml +142 -0
Linear-ODE/checkpoint.ckpt +3 -0
Linear-ODE/train.yaml +136 -0
Linear-SDE-Gamma/checkpoint.ckpt +3 -0
Linear-SDE-Gamma/train.yaml +156 -0

EncDec-ODE-Gamma/checkpoint.ckpt ADDED Viewed

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

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+size 148101112

EncDec-ODE-Gamma/train.yaml ADDED Viewed

	@@ -0,0 +1,155 @@

+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:
+              class_path: omg.si.interpolants.PeriodicEncoderDecoderInterpolant
+              init_args:
+                switch_time: 0.6487086666110259
+                power: 1.0
+            gamma:
+              class_path: omg.si.gamma.LatentGammaEncoderDecoder
+              init_args:
+                a: 1.9883383838119686
+                switch_time: 0.6487086666110259
+                power: 1.0
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 12.290317841755964
+            correct_center_of_mass_motion: true
+        # lattice vectors
+        - class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.interpolants.TrigonometricInterpolant
+            gamma:
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 0.21935645939922985
+            epsilon:
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 9.431054439782873
+                mu: 0.21809909486896933
+                sigma: 0.03292165737293197
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.001218559336848557
+            velocity_annealing_factor: 4.302804708170181
+            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: 820
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.689192251322191
+    cell_loss_b: 0.12351464867571432
+    cell_loss_z: 0.18729310000209468
+  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: 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: 0.00047748599389170053

EncDec-SDE-Gamma/checkpoint.ckpt ADDED Viewed

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

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+size 148075262

EncDec-SDE-Gamma/train.yaml ADDED Viewed

	@@ -0,0 +1,155 @@

+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:
+              class_path: omg.si.interpolants.PeriodicEncoderDecoderInterpolant
+              init_args:
+                switch_time: 0.42184997325946555
+                power: 0.5
+            gamma:
+              class_path: omg.si.gamma.LatentGammaEncoderDecoder
+              init_args:
+                a: 0.03989185248799893
+                switch_time: 0.42184997325946555
+                power: 0.5
+            epsilon:
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 2.3996529332194574
+                mu: 0.25251095399328916
+                sigma: 0.03759134500470063
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.0014076164225116372
+            velocity_annealing_factor: 3.7755089557808477
+            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: 4.961271013084809
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 1.1379701544400436
+            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: 710
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.6143090042317803
+    pos_loss_z: 0.3794040725288834
+    cell_loss_b: 0.00628692323933625
+  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: 512  # Used to be 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: 200  # Used to be 100
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 0.00018567271191860665

Linear-ODE-Gamma/checkpoint.ckpt ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
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+size 49644475

Linear-ODE-Gamma/train.yaml ADDED Viewed

	@@ -0,0 +1,142 @@

+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:
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 0.2575112227566439
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 7.7611189744870925
+            correct_center_of_mass_motion: true
+        # lattice vectors
+        - class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.interpolants.TrigonometricInterpolant
+            gamma:
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 2.9759856920732597
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 4.116061496782678
+            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: 690
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.9976417941296929
+    cell_loss_b: 0.002358205870307133
+  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: 128
+  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: 4.006249666984122e-05

Linear-ODE/checkpoint.ckpt ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
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+size 148064122

Linear-ODE/train.yaml ADDED Viewed

	@@ -0,0 +1,136 @@

+model:
+  si:
+    class_path: omg.si.StochasticInterpolants
+    init_args:
+      stochastic_interpolants:
+        # chemical species
+        - class_path: omg.si.SingleStochasticInterpolantIdentity
+        # fractional coordinates
+        - class_path: omg.si.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.PeriodicLinearInterpolant
+            gamma: null
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 10.182659004291072
+            correct_center_of_mass_motion: true
+        # lattice vectors
+        - class_path: omg.si.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.LinearInterpolant
+            gamma: null
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 1.824475401606087
+            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: 210
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.9994149341846618
+    cell_loss_b: 0.0005850658153382233
+  sampler:
+    class_path: omg.sampler.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.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+            - data/alex_mp_20/train.lmdb
+      niggli: False
+  val_dataset:
+    class_path: omg.datamodule.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+            - data/alex_mp_20/val.lmdb
+      niggli: False
+  predict_dataset:
+    class_path: omg.datamodule.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.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: 0.0006689636445843722

Linear-SDE-Gamma/checkpoint.ckpt ADDED Viewed

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

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+oid sha256:b48a00fe7e9fa442885ed8c65a06613f8d5f0f1739bc87ed1c9e7fa7d3ed3977
+size 49644475

Linear-SDE-Gamma/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.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.interpolants.PeriodicLinearInterpolant
+            gamma:
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 0.06285652866840548
+            epsilon:
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 6.097168392667226
+                mu: 0.21833859329765842
+                sigma: 0.04985718977712428
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.0032297736033797264
+            velocity_annealing_factor: 11.58289329358004
+            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: 0.1317493001266121
+            epsilon:
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 9.612495617660462
+                mu: 0.08389382419092543
+                sigma: 0.033192886798663945
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.0032297736033797264
+            velocity_annealing_factor: 5.081210983525862
+            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: 310
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.007345481151868809
+    pos_loss_z: 0.9153543617007412
+    cell_loss_b: 0.06421063793348068
+    cell_loss_z: 0.013089519213909303
+  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: 256
+  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: 0.0002629870131361822