From 6815748caa4b46cfed0e6220982748a72821b3a2 Mon Sep 17 00:00:00 2001 From: BowenD-UCB <84425382+BowenD-UCB@users.noreply.github.com> Date: Sun, 22 Oct 2023 16:56:42 -0700 Subject: [PATCH] Released 0.3.0 pretrained weights --- chgnet/data/dataset.py | 13 +- chgnet/model/model.py | 19 ++- chgnet/pretrained/0.2.0/README.md | 74 +++++++++++ .../chgnet_0.2.0_e30f77s348m32.pth.tar} | Bin chgnet/pretrained/0.3.0/README.md | 80 ++++++++++++ .../0.3.0/chgnet_0.3.0_e29f68s314m37.pth.tar | Bin 0 -> 4863221 bytes tests/test_md.py | 115 +++++++++--------- tests/test_model.py | 67 +++++----- tests/test_relaxation.py | 4 +- 9 files changed, 270 insertions(+), 102 deletions(-) create mode 100755 chgnet/pretrained/0.2.0/README.md rename chgnet/pretrained/{e30f77s348m32.pth.tar => 0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar} (100%) create mode 100755 chgnet/pretrained/0.3.0/README.md create mode 100644 chgnet/pretrained/0.3.0/chgnet_0.3.0_e29f68s314m37.pth.tar diff --git a/chgnet/data/dataset.py b/chgnet/data/dataset.py index 1d8ba35b..087dd156 100644 --- a/chgnet/data/dataset.py +++ b/chgnet/data/dataset.py @@ -48,7 +48,8 @@ def __init__( magmoms (list[list[float]], optional): [data_size, n_atoms, 1] structure_ids (list[str], optional): a list of ids to track the structures graph_converter (CrystalGraphConverter, optional): Converts the structures - to graphs. If None, it will be set to CHGNet default converter. + to graphs. If None, it will be set to CHGNet 0.3.0 converter + with AtomGraph cutoff = 6A. Raises: RuntimeError: if the length of structures and labels (energies, forces, @@ -74,7 +75,7 @@ def __init__( random.shuffle(self.keys) print(f"{len(structures)} structures imported") self.graph_converter = graph_converter or CrystalGraphConverter( - atom_graph_cutoff=5, bond_graph_cutoff=3 + atom_graph_cutoff=6, bond_graph_cutoff=3 ) self.failed_idx: list[int] = [] self.failed_graph_id: dict[str, str] = {} @@ -157,9 +158,9 @@ def __init__( labels (str, dict): the path or dictionary of labels targets ("ef" | "efs" | "efm" | "efsm"): The training targets. Default = "efsm" - graph_converter (CrystalGraphConverter, optional): - a CrystalGraphConverter to convert the structures, - if None, it will be set to CHGNet default converter + graph_converter (CrystalGraphConverter, optional): Converts the structures + to graphs. If None, it will be set to CHGNet 0.3.0 converter + with AtomGraph cutoff = 6A. energy_key (str, optional): the key of energy in the labels. Default = "energy_per_atom". force_key (str, optional): the key of force in the labels. @@ -175,7 +176,7 @@ def __init__( random.shuffle(self.cif_ids) print(f"{cif_path}: {len(self.cif_ids):,} structures imported") self.graph_converter = graph_converter or CrystalGraphConverter( - atom_graph_cutoff=5, bond_graph_cutoff=3 + atom_graph_cutoff=6, bond_graph_cutoff=3 ) self.energy_key = energy_key diff --git a/chgnet/model/model.py b/chgnet/model/model.py index 6b0bd0e1..0850807d 100644 --- a/chgnet/model/model.py +++ b/chgnet/model/model.py @@ -650,15 +650,26 @@ def from_file(cls, path, **kwargs): return CHGNet.from_dict(state["model"], **kwargs) @classmethod - def load(cls, model_name="MPtrj-efsm"): + def load(cls, model_name="0.3.0"): """Load pretrained CHGNet.""" current_dir = os.path.dirname(os.path.abspath(__file__)) - if model_name == "MPtrj-efsm": + if model_name == "0.3.0": return cls.from_file( - os.path.join(current_dir, "../pretrained/e30f77s348m32.pth.tar"), + os.path.join( + current_dir, + "../pretrained/0.3.0/chgnet_0.3.0_e29f68s314m37.pth.tar", + ) + ) + elif model_name == "0.2.0": # noqa: RET505 + return cls.from_file( + os.path.join( + current_dir, + "../pretrained/0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar", + ), mlp_out_bias=True, ) - raise ValueError(f"Unknown {model_name=}") + else: + raise ValueError(f"Unknown {model_name=}") @dataclass diff --git a/chgnet/pretrained/0.2.0/README.md b/chgnet/pretrained/0.2.0/README.md new file mode 100755 index 00000000..87ba34c0 --- /dev/null +++ b/chgnet/pretrained/0.2.0/README.md @@ -0,0 +1,74 @@ +## Model 0.2.0 + +This is the pretrained weights published with CHGNet Nature Machine Intelligence paper. +All the experiments and results shown in the paper were performed with this version of weights. + +Date: 2/24/2023 + +Author: Bowen Deng + +## Model Parameters + +```python +model = CHGNet( + atom_fea_dim=64, + bond_fea_dim=64, + angle_fea_dim=64, + composition_model="MPtrj", + num_radial=9, + num_angular=9, + n_conv=4, + atom_conv_hidden_dim=64, + update_bond=True, + bond_conv_hidden_dim=64, + update_angle=True, + angle_layer_hidden_dim=0, + conv_dropout=0, + read_out="ave", + mlp_hidden_dims=[64, 64], + mlp_first=True, + is_intensive=True, + non_linearity="silu", + atom_graph_cutoff=5, + bond_graph_cutoff=3, + graph_converter_algorithm="fast", + cutoff_coeff=5, + learnable_rbf=True, + mlp_out_bias=True, +) +``` + +## Dataset Used + +MPtrj dataset with 8-1-1 train-val-test splitting + +## Trainer + +```python +trainer = Trainer( + model=model, + targets='efsm', + energy_loss_ratio=1, + force_loss_ratio=1, + stress_loss_ratio=0.1, + mag_loss_ratio=0.1, + optimizer='Adam', + weight_decay=0, + scheduler='CosLR', + criterion='Huber', + delta=0.1, + epochs=20, + starting_epoch=0, + learning_rate=1e-3, + use_device='cuda', + print_freq=1000 +) +``` + +## Mean Absolute Error (MAE) logs + +| partition | Energy (meV/atom) | Force (meV/A) | stress (GPa) | magmom (muB) | +| ---------- | ----------------- | ------------- | ------------ | ------------ | +| Train | 22 | 59 | 0.246 | 0.030 | +| Validation | 20 | 75 | 0.350 | 0.033 | +| Test | 30 | 77 | 0.348 | 0.032 | diff --git a/chgnet/pretrained/e30f77s348m32.pth.tar b/chgnet/pretrained/0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar similarity index 100% rename from chgnet/pretrained/e30f77s348m32.pth.tar rename to chgnet/pretrained/0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar diff --git a/chgnet/pretrained/0.3.0/README.md b/chgnet/pretrained/0.3.0/README.md new file mode 100755 index 00000000..50223bbc --- /dev/null +++ b/chgnet/pretrained/0.3.0/README.md @@ -0,0 +1,80 @@ +## Model 0.3.0 + +Major changes: + +1. Increased AtomGraph cutoff to 6A +2. Resolved discontinuity issue when no BondGraph presents +3. Added some normalization layers +4. Slight improvements on energy, force, stress accuracies + +Date: 10/22/2023 + +Author: Bowen Deng + +## Model Parameters + +```python +model = CHGNet( + atom_fea_dim=64, + bond_fea_dim=64, + angle_fea_dim=64, + composition_model="MPtrj", + num_radial=31, + num_angular=31, + n_conv=4, + atom_conv_hidden_dim=64, + update_bond=True, + bond_conv_hidden_dim=64, + update_angle=True, + angle_layer_hidden_dim=0, + conv_dropout=0, + read_out="ave", + gMLP_norm='layer', + readout_norm='layer', + mlp_hidden_dims=[64, 64, 64], + mlp_first=True, + is_intensive=True, + non_linearity="silu", + atom_graph_cutoff=6, + bond_graph_cutoff=3, + graph_converter_algorithm="fast", + cutoff_coeff=8, + learnable_rbf=True, +) +``` + +## Dataset Used + +MPtrj dataset with 9-0.5-0.5 train-val-test splitting + +## Trainer + +```python +trainer = Trainer( + model=model, + targets='efsm', + energy_loss_ratio=1, + force_loss_ratio=1, + stress_loss_ratio=0.1, + mag_loss_ratio=0.1, + optimizer='Adam', + weight_decay=0, 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