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Merge branch 'master' into develop
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‎docs/sphinx/en/source/tutorial/other_models.rst

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@@ -37,7 +37,7 @@ First, prepare input_nequip.toml and set the parameters required to run NequIP.
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Below, we extract [sampling.solver] and [train] with changes from the aenet input.
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.. code-block:: toml
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[sampling.solver]
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type = 'nequip'
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base_input_dir = './baseinput_nequip'
@@ -62,37 +62,40 @@ Also, create the NequIP input file ``input.yaml`` in the ``nequip_train_input/tr
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dataset_seed: 456
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# network
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num_basis: 8
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BesselBasis_trainable: true
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AllegroBesselBasis_trainable: true
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bessel_frequency_cutoff: 4
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PolynomialCutoff_p: 6
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l_max: 1
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r_max: 8.0
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parity: true
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num_layers: 3
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num_features: 16
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nonlinearity_type: gate
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nonlinearity_scalars:
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e: silu
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o: tanh
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parity: o3_full
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num_layers: 2
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nonlinearity_gates:
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e: silu
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o: tanh
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num_tensor_features: 16
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tensors_mixing_mode: p
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two_body_latent_mlp_latent_dimensions: [32, 64]
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two_body_latent_mlp_nonlinearity: silu
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latent_mlp_latent_dimensions: [64, 64]
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latent_mlp_nonlinearity: silu
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latent_mlp_initialization: uniform
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latent_resnet: true
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env_embed_mlp_latent_dimensions: []
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env_embed_mlp_nonlinearity: null
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env_embed_mlp_initialization: uniform
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edge_eng_mlp_latent_dimensions: [16]
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edge_eng_mlp_nonlinearity: null
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edge_eng_mlp_initialization: uniform
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model_builders:
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- SimpleIrrepsConfig
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- EnergyModel
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- PerSpeciesRescale
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- RescaleEnergyEtc
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- allegro.model.Allegro
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- PerSpeciesRescale
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- RescaleEnergyEtc
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dataset: ase
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dataset_file_name: structure.xyz
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chemical_symbols:
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- Mg
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- Al
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- Mg
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- Al
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# logging
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wandb: false
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# loss function
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loss_coeffs: total_energy
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The procedure of model learning and sampling is the same as aenet.
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@@ -140,7 +144,7 @@ First, prepare input_allegro.toml and set the parameters required to run Allegro
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Below, we extract ``[sampling.solver]`` and ``[train]`` with changes from the aenet input.
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.. code-block:: toml
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[sampling.solver]
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type = 'allegro'
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base_input_dir = './baseinput_allegro'
@@ -165,16 +169,16 @@ Also, create the Allegro input file ``input.yaml`` in the ``allegro_train_input/
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dataset_seed: 456
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# network
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num_basis: 8
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BesselBasis_trainable: true
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AllegroBesselBasis_trainable: true
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bessel_frequency_cutoff: 4
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PolynomialCutoff_p: 6
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l_max: 1
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r_max: 8.0
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parity: o3_full
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num_layers: 2
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env_embed_multiplicity: 16
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embed_initial_edge: true
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num_tensor_features: 16
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tensors_mixing_mode: p
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two_body_latent_mlp_latent_dimensions: [32, 64]
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two_body_latent_mlp_nonlinearity: silu
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latent_mlp_latent_dimensions: [64, 64]
@@ -189,16 +193,16 @@ Also, create the Allegro input file ``input.yaml`` in the ``allegro_train_input/
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edge_eng_mlp_initialization: uniform
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model_builders:
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- allegro.model.Allegro
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- PerSpeciesRescale
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- RescaleEnergyEtc
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- allegro.model.Allegro
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- PerSpeciesRescale
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- RescaleEnergyEtc
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dataset: ase
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dataset_file_name: structure.xyz
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chemical_symbols:
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- Mg
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- Al
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- Mg
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- Al
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# logging
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wandb: false
@@ -291,4 +295,4 @@ Also, create the MLIP-3 input file ``input.almtp`` in the ``mlip_3_train_input/t
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alpha_moment_mapping = {0, 4, 5, 6, 7}
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The procedure of model learning and sampling is the same as aenet.
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The procedure of model learning and sampling is the same as aenet.

‎docs/sphinx/ja/source/tutorial/other_models.rst

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Original file line numberDiff line numberDiff line change
@@ -62,37 +62,40 @@ NequIP のインストール
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dataset_seed: 456
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# network
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num_basis: 8
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BesselBasis_trainable: true
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AllegroBesselBasis_trainable: true
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bessel_frequency_cutoff: 4
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PolynomialCutoff_p: 6
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l_max: 1
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r_max: 8.0
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parity: true
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num_layers: 3
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num_features: 16
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nonlinearity_type: gate
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nonlinearity_scalars:
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e: silu
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o: tanh
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parity: o3_full
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num_layers: 2
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nonlinearity_gates:
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e: silu
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o: tanh
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num_tensor_features: 16
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tensors_mixing_mode: p
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two_body_latent_mlp_latent_dimensions: [32, 64]
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two_body_latent_mlp_nonlinearity: silu
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latent_mlp_latent_dimensions: [64, 64]
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latent_mlp_nonlinearity: silu
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latent_mlp_initialization: uniform
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latent_resnet: true
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env_embed_mlp_latent_dimensions: []
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env_embed_mlp_nonlinearity: null
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env_embed_mlp_initialization: uniform
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edge_eng_mlp_latent_dimensions: [16]
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edge_eng_mlp_nonlinearity: null
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edge_eng_mlp_initialization: uniform
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model_builders:
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- SimpleIrrepsConfig
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- EnergyModel
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- PerSpeciesRescale
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- RescaleEnergyEtc
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- allegro.model.Allegro
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- PerSpeciesRescale
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- RescaleEnergyEtc
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dataset: ase
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dataset_file_name: structure.xyz
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chemical_symbols:
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- Mg
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- Al
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- Mg
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- Al
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# logging
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wandb: false
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dataset_seed: 456
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# network
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num_basis: 8
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BesselBasis_trainable: true
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AllegroBesselBasis_trainable: true
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bessel_frequency_cutoff: 4
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PolynomialCutoff_p: 6
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l_max: 1
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r_max: 8.0
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parity: o3_full
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num_layers: 2
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# num_features: 16
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env_embed_multiplicity: 16
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embed_initial_edge: true
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num_tensor_features: 16
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tensors_mixing_mode: p
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two_body_latent_mlp_latent_dimensions: [32, 64]
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two_body_latent_mlp_nonlinearity: silu
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latent_mlp_latent_dimensions: [64, 64]
@@ -190,16 +192,16 @@ Allegro のインストール
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edge_eng_mlp_initialization: uniform
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model_builders:
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- allegro.model.Allegro
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- PerSpeciesRescale
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- RescaleEnergyEtc
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- allegro.model.Allegro
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- PerSpeciesRescale
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- RescaleEnergyEtc
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dataset: ase
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dataset_file_name: structure.xyz
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chemical_symbols:
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- Mg
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- Al
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- Mg
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- Al
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# logging
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wandb: false
@@ -290,4 +292,4 @@ MLIP-3の実行ファイル ``mlp`` のパスを指定します。お使いの
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alpha_scalar_moments = 5
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alpha_moment_mapping = {0, 4, 5, 6, 7}
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モデル学習、サンプリングの方法に関してはaenetと同様です。
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モデル学習、サンプリングの方法に関してはaenetと同様です。

‎examples/active_learning_qe/allegro_train_input/train/input.yaml

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@@ -4,17 +4,16 @@ seed: 123
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dataset_seed: 456
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# network
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num_basis: 8
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BesselBasis_trainable: true
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AllegroBesselBasis_trainable: true
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bessel_frequency_cutoff: 4
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PolynomialCutoff_p: 6
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l_max: 1
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r_max: 8.0
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parity: o3_full
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num_layers: 2
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# num_features: 16
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env_embed_multiplicity: 16
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embed_initial_edge: true
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num_tensor_features: 16
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tensors_mixing_mode: p
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two_body_latent_mlp_latent_dimensions: [32, 64]
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two_body_latent_mlp_nonlinearity: silu
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latent_mlp_latent_dimensions: [64, 64]
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edge_eng_mlp_initialization: uniform
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model_builders:
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- allegro.model.Allegro
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- PerSpeciesRescale
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- RescaleEnergyEtc
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- allegro.model.Allegro
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- PerSpeciesRescale
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- RescaleEnergyEtc
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dataset: ase

‎tests/integration/active_learn_nequip/AL.sh

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#train
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echo start training
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abics_train input.toml
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# for debug
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echo '== cat train0/stdout =='
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cat train0/stdout
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echo '== end of train0/stdout =='
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echo Done

‎tests/integration/active_learn_nequip/allegro_train_input/train/input.yaml

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dataset_seed: 456
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# network
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num_basis: 8
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BesselBasis_trainable: true
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AllegroBesselBasis_trainable: true
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bessel_frequency_cutoff: 4
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PolynomialCutoff_p: 6
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l_max: 1
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r_max: 8.0
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parity: o3_full
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num_layers: 2
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# num_features: 16
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env_embed_multiplicity: 16
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embed_initial_edge: true
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num_tensor_features: 16
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tensors_mixing_mode: p
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two_body_latent_mlp_latent_dimensions: [32, 64]
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two_body_latent_mlp_nonlinearity: silu
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latent_mlp_latent_dimensions: [64, 64]
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edge_eng_mlp_initialization: uniform
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model_builders:
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- allegro.model.Allegro
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- PerSpeciesRescale
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- RescaleEnergyEtc
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- allegro.model.Allegro
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- PerSpeciesRescale
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- RescaleEnergyEtc
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dataset: ase

‎tests/integration/active_learn_nequip/install_nequip.sh

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echo
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python3 -m pip install torch
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python3 -m pip install torch==2.5.1
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python3 -m pip install nequip
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python3 -m pip install git+https://github.com/mir-group/allegro.git

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