Remove alchemical model

.github/workflows/architecture-tests.yml

@@ -11,7 +11,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - architecture-name: alchemical-model
           - architecture-name: gap
           - architecture-name: soap-bpnn
           - architecture-name: pet

CODEOWNERS

@@ -3,7 +3,6 @@
 # is modified.
 
 **/soap_bpnn            @frostedoyster
-**/alchemical_model     @abmazitov
 **/pet                  @abmazitov
 **/gap                  @DavideTisi
 **/nanopet              @frostedoyster

README.rst

@@ -64,9 +64,6 @@ model.
       Positions (SOAP).
   * - SOAP BPNN
     - A Behler-Parrinello neural network with SOAP features
-  * - Alchemical Model
-    - A Behler-Parrinello neural network with SOAP features and Alchemical Compression
-      of the composition space
   * - PET
     - Point Edge Transformer (PET), interatomic machine learning potential
 

docs/src/advanced-concepts/auxiliary-outputs.rst

@@ -31,13 +31,13 @@ by one or more architectures in the library:
 The following table shows the architectures that support each of the
 auxiliary outputs:
 
-+--------------------------------------------+-----------+------------------+-----+-----+---------+
-| Auxiliary output                           | SOAP-BPNN | Alchemical Model | PET | GAP | NanoPET |
-+--------------------------------------------+-----------+------------------+-----+-----+---------+
-| ``mtt::aux::{target}_last_layer_features`` |    Yes    |       No         | No  | No  |   Yes   |
-+--------------------------------------------+-----------+------------------+-----+-----+---------+
-| ``features``                               |    Yes    |       No         | No  | No  |   Yes   |
-+--------------------------------------------+-----------+------------------+-----+-----+---------+
++--------------------------------------------+-----------+-----+-----+---------+
+| Auxiliary output                           | SOAP-BPNN | PET | GAP | NanoPET |
++--------------------------------------------+-----------+-----+-----+---------+
+| ``mtt::aux::{target}_last_layer_features`` |    Yes    | No  | No  |   Yes   |
++--------------------------------------------+-----------+-----+-----+---------+
+| ``features``                               |    Yes    | No  | No  |   Yes   |
++--------------------------------------------+-----------+-----+-----+---------+
 
 The following tables show the metadata that will be provided for each of the
 auxiliary outputs:

docs/src/advanced-concepts/fitting-generic-targets.rst

@@ -23,11 +23,6 @@ capabilities of the architectures in metatrain.
      - Yes
      - Only with ``o3_lambda=1, o3_sigma=1``
      - No
-   * - Alchemical Model
-     - Energy, forces, stress/virial
-     - No
-     - No
-     - No
    * - GAP
      - Energy, forces
      - No

docs/src/architectures/nanopet.rst

@@ -102,6 +102,8 @@ The hyperparameters for training are
 :param scheduler_factor: Factor to reduce the learning rate by
 :param log_interval: Interval at which to log training metrics
 :param checkpoint_interval: Interval at which to save model checkpoints
+:param scale_targets: Whether to scale the targets to have unit standard deviation
+    across the training set during training.
 :param fixed_composition_weights: Weights for fixed atomic contributions to scalar
   targets
 :param per_structure_targets: Targets to calculate per-structure losses for

docs/src/architectures/soap-bpnn.rst

@@ -159,6 +159,8 @@ The parameters for training are
 :param learning_rate: learning rate
 :param log_interval: number of epochs that elapse between reporting new training results
 :param checkpoint_interval: Interval to save a checkpoint to disk.
+:param scale_targets: Whether to scale the targets to have unit standard deviation
+    across the training set during training.
 :param fixed_composition_weights: allows to set fixed isolated atom energies from
     outside. These are per target name and per (integer) atom type. For example,
     ``fixed_composition_weights: {"energy": {1: -396.0, 6: -500.0}, "mtt::U0": {1: 0.0,

docs/src/dev-docs/utils/scaler.rst

@@ -0,0 +1,7 @@
+Scaler
+######
+
+.. automodule:: metatrain.utils.scaler
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/static/refs.bib

@@ -42,29 +42,6 @@ @article{willatt_feature_2018
   langid = {english}
 }
 
-@article{mazitov_surface_2024,
-	author={Mazitov, Arslan and Springer, Maximilian A. and Lopanitsyna, Nataliya and Fraux, Guillaume and De, Sandip and Ceriotti, Michele},
-	title={Surface segregation in high-entropy alloys from alchemical machine learning},
-	journal={Journal of Physics: Materials},
-	url={http://iopscience.iop.org/article/10.1088/2515-7639/ad2983},
-	year={2024},
-}
-
-@article{lopanitsyna_modeling_2023,
-  title = {Modeling high-entropy transition metal alloys with alchemical compression},
-  author = {Lopanitsyna, Nataliya and Fraux, Guillaume and Springer, Maximilian A. and De, Sandip and Ceriotti, Michele},
-  journal = {Phys. Rev. Mater.},
-  volume = {7},
-  issue = {4},
-  pages = {045802},
-  numpages = {15},
-  year = {2023},
-  month = {Apr},
-  publisher = {American Physical Society},
-  doi = {10.1103/PhysRevMaterials.7.045802},
-  url = {https://link.aps.org/doi/10.1103/PhysRevMaterials.7.045802}
-}
-
 @article{bigi_smooth_2022,
     author = {Bigi, Filippo and Huguenin-Dumittan, Kevin K. and Ceriotti, Michele and Manolopoulos, David E.},
     title = "{A smooth basis for atomistic machine learning}",

pyproject.toml

@@ -61,9 +61,6 @@ build-backend = "setuptools.build_meta"
 soap-bpnn = [
     "rascaline-torch @ git+https://github.com/luthaf/rascaline@5326b6e#subdirectory=python/rascaline-torch",
 ]
-alchemical-model = [
-  "torch_alchemical @ git+https://github.com/abmazitov/torch_alchemical.git@51ff519",
-]
 pet = [
   "pet @ git+https://github.com/lab-cosmo/pet@5d40710",
 ]