[UPLOAD] Trained model with PBE added to repo

.gitattributes

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+# yamllint disable rule:line-length
+# This tutorial config file is meant to complement the "User Guide" docs: https://nequip.readthedocs.io/en/latest/guide/guide.html
+# New users are advised to read the config page before continuing: https://nequip.readthedocs.io/en/latest/guide/configuration/config.html
+
+# ===========
+#     RUN
+# ===========
+# the run types will be completed in sequence
+# one can do `train`, `val`, `test` run types
+run: [train, test]
+
+
+# the following parameters (cutoff_radius, chemical_symbols, model_type_names, monitored_metric) are not used directly by the config parser
+# but parameters that should share the same values are present in different parts of the config
+# thus, we use variable interpolation to keep their multiple instances consistent
+# i.e. we only ever have to change the values here instead of everywhere it's necessary to
+
+# data and model r_max can be different (model's r_max should be smaller), but we try to make them the same
+cutoff_radius: 5.0
+
+# variable interpolation is convenient for wandb sweeps, see documentation for more details
+# the following are NequIP model hyperparameters that can be swept over
+num_layers: 4       # number of interaction blocks, we find 3-5 to work best
+l_max: 1            # the maximum irrep order (rotation order) for the network's features, l=1 is a good default, l=2 is more accurate but slower
+num_features: 32    # the multiplicity of the features, 32 is a good default for accurate network, if you want to be more accurate, go larger, if you want to be faster, go lower
+
+# There are two sets of atomic types to keep track of in most applications.
+# There is the conventional atomic species (e.g. C, H), and a separate `type_names` known to the model.
+# The model only knows types based on a set of zero-based indices and user-given `type_names` argument.
+# An example where this distinction is necessary include datasets with the same atomic species with different charge states:
+# we could define `chemical_species: [C, C]` and model `type_names: [C3, C4]` for +3 and +4 charge states.
+# There could also be instances such as coarse graining we only care about the model's `type_names` (no need to define chemical species).
+# Because of this distinction, these variables show up as arguments across different categories, including, data, model, metrics and even callbacks.
+# In this case, we fix both to be the same, so we define a single set of each here and use variable interpolation to retrieve them below.
+# This ensures a single location where the values are set to reduce the chances of misconfiguring runs.
+model_type_names: [C, H, O, Cu]
+chemical_species: ${model_type_names}
+
+# We want a metric to condition training on (e.g. for best `ModelCheckpoint`, `EarlyStopping`, LR scheduling) which will show up in various places later on, so we set up a "single source of truth" to interpolate over
+# see https://nequip.readthedocs.io/en/latest/guide/configuration/metrics.html
+monitored_metric: val0_epoch/weighted_sum
+
+
+# ============
+#     DATA
+# ============
+# New users are advised to read the "Data Configuration" docs before continuing: https://nequip.readthedocs.io/en/latest/guide/configuration/data.html
+data:
+  _target_: nequip.data.datamodule.ASEDataModule
+  seed: 456             # dataset seed for reproducibility
+
+  # here we take an ASE-readable file (in extxyz format) and split it into train:val:test = 80:10:10
+  split_dataset:
+    file_path: FragmentChainExtension/FragmentChainExtensionTraining.xyz
+    train: 0.9
+    val: 0.05
+    test: 0.05
+
+  # `transforms` convert data from the Dataset to a form that can be used by the ML model
+  transforms:
+    # the models only know atom types, which can be different from the chemical species (e.g. C, H)
+    # in this case, the atom types are the same as the chemical species (H, C, O, Cu), so we can omit
+    # `chemical_species_to_atom_type_map` and it will default to an identity mapping
+    # if `model_type_names` were something like ["my_H", "carbon", "oxygen", "copper"], then you would need
+    # to explicitly provide the mapping: chemical_species_to_atom_type_map: {H: my_H, C: carbon, O: oxygen, Cu: copper}
+    - _target_: nequip.data.transforms.ChemicalSpeciesToAtomTypeMapper
+      model_type_names: ${model_type_names}
+      # chemical_species_to_atom_type_map: ${list_to_identity_dict:${chemical_species}}
+    # data doesn't usually come with a neighborlist -- this transform prepares the neighborlist
+    - _target_: nequip.data.transforms.NeighborListTransform
+      r_max: ${cutoff_radius}
+
+  # the following are torch.utils.data.DataLoader configs,
+  # excluding the arguments `dataset` and `collate_fn`
+  # https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader
+  train_dataloader:
+    _target_: torch.utils.data.DataLoader
+    batch_size: 5
+    num_workers: 5
+    shuffle: true
+  val_dataloader:
+    _target_: torch.utils.data.DataLoader
+    batch_size: 10
+    num_workers: ${data.train_dataloader.num_workers}  # we want to use the same num_workers -- variable interpolation helps
+  test_dataloader: ${data.val_dataloader}  # variable interpolation comes in handy again
+
+  # dataset statistics can be calculated to be used for model initialization such as for shifting, scaling and standardizing.
+  # it is advised to provide custom names -- you will have to retrieve them later under model to initialize certain parameters to the dataset statistics computed
+  stats_manager:
+    # dataset statistics is handled by the `DataStatisticsManager`
+    # here, we use `CommonDataStatisticsManager` for a basic set of dataset statistics for general use cases
+    # the dataset statistics include `num_neighbors_mean`, `per_atom_energy_mean`, `forces_rms`, `per_type_forces_rms`
+    _target_: nequip.data.CommonDataStatisticsManager
+    # dataloader kwargs for data statistics computation
+    # `batch_size` should ideally be as large as possible without triggering OOM
+    dataloader_kwargs:
+      batch_size: 10
+    # we need to provide the same type names that correspond to the model's `type_names`
+    # so we interpolate the "central source of truth" model type names from above
+    type_names: ${model_type_names}
+
+
+# =============
+#    TRAINER
+# =============
+# `trainer` is a `Lightning.Trainer` object (https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api)
+trainer:
+  _target_: lightning.Trainer
+  accelerator: gpu
+  enable_checkpointing: true
+  max_epochs: 1000
+  max_time: 03:00:00:00
+  log_every_n_steps: 20       # how often to log
+
+  # use any Lightning supported logger
+  logger:
+    # Lightning wandb logger https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb
+    _target_: lightning.pytorch.loggers.wandb.WandbLogger
+    project: nequip
+    name: tutorial
+    save_dir: ${hydra:runtime.output_dir}  # use resolver to place wandb logs in hydra's output directory
+
+  # use any Lightning callbacks https://lightning.ai/docs/pytorch/stable/api_references.html#callbacks
+  # and any custom callbacks that subclass Lightning's Callback parent class
+  callbacks:
+
+    # https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html#lightning.pytorch.callbacks.EarlyStopping
+    - _target_: lightning.pytorch.callbacks.EarlyStopping
+      monitor: ${monitored_metric}            # validation metric to monitor
+      min_delta: 1e-3                         # how much to be considered a "change"
+      patience: 20                            # how many instances of "no change" before stopping
+
+    # https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html#lightning.pytorch.callbacks.ModelCheckpoint
+    - _target_: lightning.pytorch.callbacks.ModelCheckpoint
+      monitor: ${monitored_metric}            # validation metric to monitor
+      dirpath: ${hydra:runtime.output_dir}    # use hydra output directory
+      filename: best                          # `best.ckpt` is the checkpoint name
+      save_last: true                         # `last.ckpt` will be saved
+
+    # https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.LearningRateMonitor.html#lightning.pytorch.callbacks.LearningRateMonitor
+    - _target_: lightning.pytorch.callbacks.LearningRateMonitor
+      logging_interval: epoch
+
+
+# =====================
+#    TRAINING MODULE
+# =====================
+# training_module refers to a `NequIPLightningModule` or its subclass
+# here we use the subclass that holds an exponential moving average of the base model's weights (an EMA model)
+# one could also use the base `NequIPLightningModule` here if one does not want to use an EMA model
+# EMA allows for smoother validation curves and thus more reliable metrics for monitoring
+# Loading from a checkpoint for use in the `nequip.ase.NequIPCalculator` or during `nequip-compile` and `nequip-package` will always load the EMA model if it's present
+training_module:
+  _target_: nequip.train.EMALightningModule
+
+  # the ema decay parameter of an EMA model
+  ema_decay: 0.999
+
+  # New users are advised to read the "Loss and Metrics" docs before continuing: https://nequip.readthedocs.io/en/latest/guide/configuration/metrics.html
+  loss:
+    _target_: nequip.train.EnergyForceLoss
+    per_atom_energy: true
+    coeffs:
+      total_energy: 1.0
+      forces: 1.0
+
+  val_metrics:
+    _target_: nequip.train.EnergyForceMetrics
+    coeffs:
+      total_energy_mae: 1.0
+      forces_mae: 1.0
+      # keys `total_energy_rmse` and `forces_rmse`, `per_atom_energy_rmse` and `per_atom_energy_mae` are also available
+  # we could have train_metrics and test_metrics be different from val_metrics, but it makes sense to have them be the same
+  train_metrics: ${training_module.val_metrics}  # use variable interpolation
+  test_metrics: ${training_module.val_metrics}  # use variable interpolation
+
+  # any torch compatible optimizer: https://pytorch.org/docs/stable/optim.html#algorithms
+  optimizer:
+    _target_: torch.optim.Adam
+    lr: 0.01
+
+  # see options for lr_scheduler_config
+  # https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.core.LightningModule.html#lightning.pytorch.core.LightningModule.configure_optimizers
+  lr_scheduler:
+    # any torch compatible lr scheduler
+    scheduler:
+      _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
+      factor: 0.6
+      patience: 5
+      threshold: 0.2
+      min_lr: 1e-6
+    monitor: ${monitored_metric}
+    interval: epoch
+    frequency: 1
+
+  # model: https://nequip.readthedocs.io/en/latest/api/nequip_model.html
+  model:
+    _target_: nequip.model.NequIPGNNModel
+
+    # If you have PyTorch >= 2.6.0 installed, and are training on GPUs, the following line uses torch.compile to speed up training
+    # for more details, see https://nequip.readthedocs.io/en/latest/guide/accelerations/pt2_compilation.html
+    compile_mode: compile
+    # ^ if you're using PyTorch <= 2.6.0, an error will be thrown -- comment out the line to avoid it
+
+    # == basic model params ==
+    seed: 456
+    model_dtype: float32
+    type_names: ${model_type_names}
+    r_max: ${cutoff_radius}
+
+    # == bessel encoding ==
+    num_bessels: 8                # number of basis functions used in the radial Bessel basis, the default of 8 usually works well
+    bessel_trainable: false       # set true to train the bessel weights (default false)
+    polynomial_cutoff_p: 6        # p-exponent used in polynomial cutoff function, smaller p corresponds to stronger decay with distance
+
+    # == convnet layers ==
+    num_layers: ${num_layers}      # number of interaction blocks, we find 3-5 to work best
+    l_max: ${l_max}                # the maximum irrep order (rotation order) for the network's features, l=1 is a good default, l=2 is more accurate but slower
+    parity: true                   # whether to include features with odd mirror parity; often turning parity off gives equally good results but faster networks, so do consider this
+    num_features: ${num_features}  # the multiplicity of the features, 32 is a good default for accurate network, if you want to be more accurate, go larger, if you want to be faster, go lower
+
+    # it is also possible to provide the multiplicity for each irrep, e.g. for l_max=1 and parity=False, the following refers to 5x0e + 2x1o features
+    # num_features: [5, 2]
+
+    # == radial network ==
+    radial_mlp_depth: 2            # number of radial layers, usually 1-3 works best, smaller is faster
+    radial_mlp_width: 64           # number of hidden neurons in radial function, smaller is faster
+    # ^ we could have programatically set `radial_mlp_width` to be twice `num_features` using NequIP's built in `int_mul` resolver, e.g.
+    # radial_mlp_width: ${int_mul:${num_features},2}
+    # the NequIP framework implements `int_mul` and `int_div`
+
+    # see https://nequip.readthedocs.io/en/latest/guide/configuration/model.html to understand the following hyperparameters
+
+    # dataset statistics used to inform the model's initial parameters for normalization, shifting and rescaling
+    # we use omegaconf's resolvers (https://omegaconf.readthedocs.io/en/2.3_branch/usage.html#resolvers)
+    # to facilitate getting the dataset statistics from the `DataStatisticsManager`
+
+    # average number of neighbors for edge sum normalization
+    avg_num_neighbors: ${training_data_stats:num_neighbors_mean}
+
+    # == per-type per-atom scales and shifts ==
+    per_type_energy_scales: ${training_data_stats:per_type_forces_rms}
+    per_type_energy_shifts: ${training_data_stats:per_atom_energy_mean}
+    # ^ IMPORTANT: it is usually useful and important to use isolated atom energies computed with the same method used to generate the training data
+    #   they should be provided as a dict, e.g.
+    # per_type_energy_shifts:
+    #   C: 1.234
+    #   H: 2.345
+    #   O: 3.456
+    #   Cu: 4.567
+    per_type_energy_scales_trainable: false
+    per_type_energy_shifts_trainable: false
+
+    # == ZBL pair potential ==
+    # useful as a prior for core repulsion to mitigate MD failure modes associated with atoms getting too close
+    # docs: https://nequip.readthedocs.io/en/latest/api/nn.html#nequip.nn.pair_potential.ZBL
+    pair_potential:
+      _target_: nequip.nn.pair_potential.ZBL
+      units: metal     # Ang and kcal/mol; LAMMPS unit names;  allowed values "metal" and "real"
+      chemical_species: ${chemical_species}   # must tell ZBL the chemical species of the various model atom types

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