Upload Simergy diffusion TMO alpha checkpoint

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README.md

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+---
+base_model: microsoft/mattergen
+library_name: mattergen
+tags:
+- diffusion
+- mattergen
+- crystal-generation
+- materials-science
+- transition-metal-oxides
+- energy-above-hull
+---
+
+# Simergy Diffusion TMO Alpha
+
+Fine-tuned MatterGen diffusion checkpoint for transition-metal oxide crystal generation. The model was full-finetuned from the MatterGen `chemical_system_energy_above_hull` checkpoint and is conditioned on:
+
+- `chemical_system`
+- `energy_above_hull`
+
+Recommended diffusion guidance factor: `9.0`.
+
+## Files
+
+- `config.yaml`: MatterGen/Hydra config for loading this checkpoint directory.
+- `checkpoints/last.ckpt`: final training checkpoint, used by MatterGen when `checkpoint_epoch="last"`.
+- `checkpoints/epoch=59-loss_val=0.36.ckpt`: best monitored validation checkpoint.
+- `metrics.csv`: training and validation metrics emitted by Lightning.
+- `training_overrides.yaml`: Hydra overrides used for the finetuning run.
+- `hparams.yaml`: Lightning hparams file.
+
+## Usage
+
+Download the repository and pass the snapshot directory as MatterGen `model_path`:
+
+```python
+from huggingface_hub import snapshot_download
+
+model_dir = snapshot_download("HishaamA/simergy-diffusion-tmo-alpha")
+print(model_dir)
+```
+
+Then generate with MatterGen:
+
+```bash
+python -m mattergen.scripts.generate ./results/tmo_samples \
+  --model_path="$MODEL_DIR" \
+  --checkpoint_epoch=last \
+  --properties_to_condition_on="{'chemical_system':'Li-O','energy_above_hull':0.05}" \
+  --diffusion_guidance_factor=9.0 \
+  --record_trajectories=False
+```
+
+For the best monitored checkpoint, use `--checkpoint_epoch=best`.
+
+## Training Summary
+
+- Base checkpoint: `chemical_system_energy_above_hull`
+- Finetuning mode: full finetuning
+- Dataset config: `tmo_ehull`
+- Trainer config: `single_gpu`
+- Max epochs: `100`
+- Accumulated gradient batches: `8`
+- Learning rate: `5e-6`
+
+Generated structures should be independently screened and validated before any downstream scientific or engineering use.

checkpoints/epoch=59-loss_val=0.36.ckpt

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checkpoints/last.ckpt

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config.yaml

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+adapter:
+  adapter:
+    _target_: mattergen.adapter.GemNetTAdapter
+    atom_type_diffusion: mask
+    denoise_atom_types: true
+    gemnet:
+      _target_: mattergen.common.gemnet.gemnet_ctrl.GemNetTCtrl
+      atom_embedding:
+        _target_: mattergen.common.gemnet.layers.embedding_block.AtomEmbedding
+        emb_size: 512
+        with_mask_type: true
+      condition_on_adapt: []
+      cutoff: 7.0
+      emb_size_atom: 512
+      emb_size_edge: 512
+      latent_dim: 512
+      max_cell_images_per_dim: 5
+      max_neighbors: 50
+      num_blocks: 4
+      num_targets: 1
+      otf_graph: true
+      regress_stress: true
+      scale_file: /scratch/amlt_code/mattergen/common/gemnet/gemnet-dT.json
+    hidden_dim: 512
+    property_embeddings: {}
+    property_embeddings_adapt: {}
+  full_finetuning: true
+  load_epoch: last
+  model_path: null
+  pretrained_name: chemical_system_energy_above_hull
+data_module:
+  _recursive_: true
+  _target_: mattergen.common.data.datamodule.CrystDataModule
+  average_density: 0.06
+  batch_size:
+    test: 16
+    train: 16
+    val: 16
+  dataset_transforms:
+  - _partial_: true
+    _target_: mattergen.common.data.dataset_transform.filter_sparse_properties
+  num_workers:
+    test: 0
+    train: 4
+    val: 0
+  properties:
+  - energy_above_hull
+  root_dir: /home/sagemaker-user/simergy-diffusion-tmo-alpha/mattergen_repo/mattergen/../datasets/cache/tmo_ehull
+  test_dataset:
+    _target_: mattergen.common.data.dataset.CrystalDataset.from_cache_path
+    cache_path: /home/sagemaker-user/simergy-diffusion-tmo-alpha/mattergen_repo/mattergen/../datasets/cache/tmo_ehull/val
+    dataset_transforms:
+    - _partial_: true
+      _target_: mattergen.common.data.dataset_transform.filter_sparse_properties
+    properties:
+    - energy_above_hull
+    transforms:
+    - _partial_: true
+      _target_: mattergen.common.data.transform.symmetrize_lattice
+    - _partial_: true
+      _target_: mattergen.common.data.transform.set_chemical_system_string
+  train_dataset:
+    _target_: mattergen.common.data.dataset.CrystalDataset.from_cache_path
+    cache_path: /home/sagemaker-user/simergy-diffusion-tmo-alpha/mattergen_repo/mattergen/../datasets/cache/tmo_ehull/train
+    dataset_transforms:
+    - _partial_: true
+      _target_: mattergen.common.data.dataset_transform.filter_sparse_properties
+    properties:
+    - energy_above_hull
+    transforms:
+    - _partial_: true
+      _target_: mattergen.common.data.transform.symmetrize_lattice
+    - _partial_: true
+      _target_: mattergen.common.data.transform.set_chemical_system_string
+  transforms:
+  - _partial_: true
+    _target_: mattergen.common.data.transform.symmetrize_lattice
+  - _partial_: true
+    _target_: mattergen.common.data.transform.set_chemical_system_string
+  val_dataset:
+    _target_: mattergen.common.data.dataset.CrystalDataset.from_cache_path
+    cache_path: /home/sagemaker-user/simergy-diffusion-tmo-alpha/mattergen_repo/mattergen/../datasets/cache/tmo_ehull/val
+    dataset_transforms:
+    - _partial_: true
+      _target_: mattergen.common.data.dataset_transform.filter_sparse_properties
+    properties:
+    - energy_above_hull
+    transforms:
+    - _partial_: true
+      _target_: mattergen.common.data.transform.symmetrize_lattice
+    - _partial_: true
+      _target_: mattergen.common.data.transform.set_chemical_system_string
+lightning_module:
+  _target_: mattergen.diffusion.lightning_module.DiffusionLightningModule
+  diffusion_module:
+    _target_: mattergen.diffusion.diffusion_module.DiffusionModule
+    corruption:
+      _target_: mattergen.diffusion.corruption.multi_corruption.MultiCorruption
+      discrete_corruptions:
+        atomic_numbers:
+          _target_: mattergen.diffusion.corruption.d3pm_corruption.D3PMCorruption
+          d3pm:
+            _target_: mattergen.diffusion.d3pm.d3pm.MaskDiffusion
+            dim: 101
+            schedule:
+              _target_: mattergen.diffusion.d3pm.d3pm.create_discrete_diffusion_schedule
+              kind: standard
+              num_steps: 1000
+          offset: 1
+      sdes:
+        cell:
+          _target_: mattergen.common.diffusion.corruption.LatticeVPSDE.from_vpsde_config
+          vpsde_config:
+            beta_max: 20
+            beta_min: 0.1
+            limit_density: 0.05771451654022283
+            limit_var_scaling_constant: 0.25
+        pos:
+          _target_: mattergen.common.diffusion.corruption.NumAtomsVarianceAdjustedWrappedVESDE
+          limit_info_key: num_atoms
+          sigma_max: 5.0
+          wrapping_boundary: 1.0
+    loss_fn:
+      _target_: mattergen.common.loss.MaterialsLoss
+      d3pm_hybrid_lambda: 0.01
+      include_atomic_numbers: true
+      include_cell: true
+      include_pos: true
+      reduce: sum
+      weights:
+        atomic_numbers: 1.0
+        cell: 1.0
+        pos: 0.1
+    model:
+      _target_: mattergen.adapter.GemNetTAdapter
+      atom_type_diffusion: mask
+      denoise_atom_types: true
+      gemnet:
+        _target_: mattergen.common.gemnet.gemnet_ctrl.GemNetTCtrl
+        atom_embedding:
+          _target_: mattergen.common.gemnet.layers.embedding_block.AtomEmbedding
+          emb_size: 512
+          with_mask_type: true
+        condition_on_adapt: []
+        cutoff: 7.0
+        emb_size_atom: 512
+        emb_size_edge: 512
+        latent_dim: 512
+        max_cell_images_per_dim: 5
+        max_neighbors: 50
+        num_blocks: 4
+        num_targets: 1
+        otf_graph: true
+        regress_stress: true
+        scale_file: /scratch/amlt_code/mattergen/common/gemnet/gemnet-dT.json
+      hidden_dim: 512
+      property_embeddings: {}
+      property_embeddings_adapt: {}
+    pre_corruption_fn:
+      _target_: mattergen.property_embeddings.SetEmbeddingType
+      dropout_fields_iid: false
+      p_unconditional: 0.2
+  optimizer_partial:
+    _partial_: true
+    _target_: torch.optim.Adam
+    lr: 5.0e-06
+  scheduler_partials:
+  - frequency: 1
+    interval: epoch
+    monitor: loss_train
+    scheduler:
+      _partial_: true
+      _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
+      factor: 0.6
+      min_lr: 1.0e-06
+      patience: 100
+      verbose: true
+    strict: true
+trainer:
+  _target_: pytorch_lightning.Trainer
+  accelerator: gpu
+  accumulate_grad_batches: 8
+  callbacks:
+  - _target_: pytorch_lightning.callbacks.ModelCheckpoint
+    every_n_epochs: 1
+    filename: '{epoch}-{loss_val:.2f}'
+    mode: min
+    monitor: loss_val
+    save_last: true
+    save_top_k: 1
+    verbose: false
+  - _target_: pytorch_lightning.callbacks.TQDMProgressBar
+    refresh_rate: 50
+  - _target_: mattergen.common.data.callback.SetPropertyScalers
+  check_val_every_n_epoch: 5
+  devices: 1
+  gradient_clip_algorithm: value
+  gradient_clip_val: 0.5
+  log_every_n_steps: 10
+  logger:
+    _target_: pytorch_lightning.loggers.CSVLogger
+    name: tmo_ehull_finetune
+    save_dir: /home/sagemaker-user/simergy-diffusion-tmo-alpha/results/tmo_ehull_finetune
+  max_epochs: 100
+  num_nodes: 1
+  precision: 32

hparams.yaml

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+{}

metrics.csv

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training_overrides.yaml

@@ -0,0 +1,7 @@
+- trainer=single_gpu
+- data_module=tmo_ehull
+- adapter.pretrained_name=chemical_system_energy_above_hull
+- adapter.full_finetuning=true
+- trainer.max_epochs=100
+- trainer.accumulate_grad_batches=8
+- lightning_module.optimizer_partial.lr=5e-6