Remove redundant attributes and kwargs now that the species are encoded in future models

bin/emle-server

@@ -58,10 +58,6 @@ try:
 except:
     port = None
 model = os.getenv("EMLE_MODEL")
-try:
-    species = [int(x) for x in os.getenv("EMLE_SPECIES").split(",")]
-except:
-    species = None
 method = os.getenv("EMLE_METHOD")
 alpha_mode = os.getenv("EMLE_ALPHA_MODE")
 atomic_numbers = os.getenv("EMLE_ATOMIC_NUMBERS")
@@ -134,7 +130,6 @@ env = {
     "host": host,
     "port": port,
     "model": model,
-    "species": species,
     "method": method,
     "alpha_mode": alpha_mode,
     "atomic_numbers": atomic_numbers,
@@ -191,13 +186,6 @@ parser.add_argument("--port", type=str, help="the port number", required=False)
 parser.add_argument(
     "--model", type=str, help="path to an EMLE model file", required=False
 )
-parser.add_argument(
-    "--species",
-    type=str,
-    nargs="*",
-    help="the species supported by the model",
-    required=False,
-)
 parser.add_argument(
     "--method",
     type=str,

emle/_utils.py

@@ -31,6 +31,7 @@ def _fetch_resources():
 
     import os as _os
     import pygit2 as _pygit2
+    import sys
 
     # Create the name for the expected resources directory.
     resource_dir = _os.path.join(
@@ -40,7 +41,7 @@ def _fetch_resources():
     # Check if the resources directory exists.
     if not _os.path.exists(resource_dir):
         # If it doesn't, clone the resources repository.
-        print("Downloading EMLE resources...")
+        print("Downloading EMLE resources...", file=sys.stderr)
         _pygit2.clone_repository(
             "https://github.com/chemle/emle-models.git", resource_dir
         )

emle/calculator.py

@@ -90,7 +90,6 @@ class EMLECalculator:
     def __init__(
         self,
         model=None,
-        species=None,
         method="electrostatic",
         alpha_mode="species",
         atomic_numbers=None,
@@ -127,10 +126,6 @@ def __init__(
             Path to the EMLE embedding model parameter file. If None, then a
             default model will be used.
 
-        species: List[int]
-            List of species (atomic numbers) supported by the EMLE model. If
-            None, then the default species list will be used.
-
         method: str
             The desired embedding method. Options are:
                 "electrostatic":
@@ -418,7 +413,6 @@ def __init__(
         self._emle = _EMLE(
             model=model,
             method=method,
-            species=species,
             alpha_mode=alpha_mode,
             atomic_numbers=atomic_numbers,
             mm_charges=self._mm_charges,
@@ -795,7 +789,6 @@ def __init__(
             # Create an MM EMLE model for interpolation.
             self._emle_mm = _EMLE(
                 model=model,
-                species=species,
                 alpha_mode=alpha_mode,
                 atomic_numbers=atomic_numbers,
                 method="mm",
@@ -941,7 +934,6 @@ def __init__(
 
         # Get the settings from the internal EMLE model.
         self._model = self._emle._model
-        self._species = self._emle._species
         self._method = self._emle._method
         self._alpha_mode = self._emle._alpha_mode
         self._atomic_numbers = self._emle._atomic_numbers
@@ -952,7 +944,6 @@ def __init__(
         # Store the settings as a dictionary.
         self._settings = {
             "model": None if model is None else self._model,
-            "species": None if species is None else self._species,
             "method": self._method,
             "alpha_mode": self._alpha_mode,
             "atomic_numbers": None if atomic_numbers is None else atomic_numbers,
@@ -1052,21 +1043,10 @@ def run(self, path=None):
             xyz_mm = _np.append(xyz_mm, xyz_mm_pad, axis=0)
             charges_mm = _np.append(charges_mm, charges_mm_pad)
 
-        # Convert the QM atomic numbers to elements and species IDs.
-        species_id = []
+        # Convert the QM atomic numbers to elements.
         elements = []
         for id in atomic_numbers:
-            try:
-                species_id.append(self._species.index(id))
-                elements.append(_ase.Atom(id).symbol)
-            except:
-                msg = (
-                    f"Unsupported element index '{id}'. "
-                    f"The current model supports {', '.join(self._supported_elements)}"
-                )
-                _logger.error(msg)
-                raise ValueError(msg)
-        self._species_id = _torch.tensor(_np.array(species_id), device=self._device)
+            elements.append(_ase.Atom(id).symbol)
 
         # First try to use the specified backend to compute in vacuo
         # energies and (optionally) gradients.
@@ -1437,21 +1417,10 @@ def _sire_callback(self, atomic_numbers, charges_mm, xyz_qm, xyz_mm, idx_mm=None
             xyz_mm = _np.append(xyz_mm, xyz_mm_pad, axis=0)
             charges_mm = _np.append(charges_mm, charges_mm_pad)
 
-        # Convert the QM atomic numbers to elements and species IDs.
-        species_id = []
+        # Convert the QM atomic numbers to elements.
         elements = []
         for id in atomic_numbers:
-            try:
-                species_id.append(self._species.index(id))
-                elements.append(_ase.Atom(id).symbol)
-            except:
-                msg = (
-                    f"Unsupported element index '{id}'. "
-                    f"The current model supports {', '.join(self._supported_elements)}"
-                )
-                _logger.error(msg)
-                raise ValueError(msg)
-        self._species_id = _torch.tensor(_np.array(species_id), device=self._device)
+            elements.append(_ase.Atom(id).symbol)
 
         # First try to use the specified backend to compute in vacuo
         # energies and (optionally) gradients.
@@ -1774,7 +1743,6 @@ def _sire_callback_optimised(
             # Create the model.
             ani2x_emle = _ANI2xEMLE(
                 emle_model=self._model,
-                emle_species=self._species,
                 alpha_mode=self._alpha_mode,
                 mm_charges=self._mm_charges,
                 model_index=self._ani2x_model_index,

emle/models/_ani.py

@@ -58,7 +58,6 @@ def __init__(
         self,
         emle_model=None,
         emle_method="electrostatic",
-        emle_species=None,
         alpha_mode="species",
         mm_charges=None,
         model_index=None,
@@ -90,10 +89,6 @@ def __init__(
                     MM charges are used for the core charge and valence charges
                     are set to zero.
 
-        emle_species: List[int], Tuple[int], numpy.ndarray, torch.Tensor
-            List of species (atomic numbers) supported by the EMLE model. If
-            None, then the default species list will be used.
-
         alpha_mode: str
             How atomic polarizabilities are calculated.
                 "species":
@@ -173,7 +168,6 @@ def __init__(
         self._emle = _EMLE(
             model=emle_model,
             method=emle_method,
-            species=emle_species,
             alpha_mode=alpha_mode,
             atomic_numbers=(atomic_numbers if atomic_numbers is not None else None),
             mm_charges=mm_charges,

emle/models/_emle.py

@@ -83,7 +83,6 @@ def __init__(
         self,
         model=None,
         method="electrostatic",
-        species=None,
         alpha_mode="species",
         atomic_numbers=None,
         mm_charges=None,
@@ -116,10 +115,6 @@ def __init__(
                     should also specify the MM charges for atoms in the QM
                     region.
 
-        species: List[int], Tuple[int], numpy.ndarray, torch.Tensor
-            List of species (atomic numbers) supported by the EMLE model. If
-            None, then the default species list will be used.
-
         alpha_mode: str
             How atomic polarizabilities are calculated.
                 "species":
@@ -220,26 +215,6 @@ def __init__(
             if not _os.path.isfile(abs_model):
                 raise IOError(f"Unable to locate EMLE embedding model file: '{model}'")
             self._model = abs_model
-
-            # Validate the species for the custom model.
-            if species is not None:
-                if isinstance(species, (_np.ndarray, _torch.Tensor)):
-                    species = species.tolist()
-                if not isinstance(species, (tuple, list)):
-                    raise TypeError(
-                        "'species' must be of type 'list', 'tuple', or 'numpy.ndarray'"
-                    )
-                if not all(isinstance(s, int) for s in species):
-                    raise TypeError("All elements of 'species' must be of type 'int'")
-                if not all(s > 0 for s in species):
-                    raise ValueError(
-                        "All elements of 'species' must be greater than zero"
-                    )
-                # Use the custom species.
-                self._species = species
-            else:
-                # Use the default species.
-                species = self._species
         else:
             # Set to None as this will be used in any calculator configuration.
             self._model = None

emle/models/_emle_base.py

@@ -173,7 +173,8 @@ def __init__(
                 )
             if not isinstance(aev_computer, tuple(allowed_types)):
                 raise TypeError(
-                    "'aev_computer' must be of type 'torchani.AEVComputer' or 'NNPOps.SymmetryFunctions.TorchANISymmetryFunctions'"
+                    "'aev_computer' must be of type 'torchani.AEVComputer' or "
+                    "'NNPOps.SymmetryFunctions.TorchANISymmetryFunctions'"
                 )
             self._aev_computer = aev_computer
         else:

emle/models/_mace.py

@@ -64,7 +64,6 @@ def __init__(
         self,
         emle_model=None,
         emle_method="electrostatic",
-        emle_species=None,
         alpha_mode="species",
         mm_charges=None,
         mace_model=None,
@@ -95,10 +94,6 @@ def __init__(
                     MM charges are used for the core charge and valence charges
                     are set to zero.
 
-        emle_species: List[int], Tuple[int], numpy.ndarray, torch.Tensor
-            List of species (atomic numbers) supported by the EMLE model. If
-            None, then the default species list will be used.
-
         alpha_mode: str
             How atomic polarizabilities are calculated.
                 "species":
@@ -175,7 +170,6 @@ def __init__(
         self._emle = _EMLE(
             model=emle_model,
             method=emle_method,
-            species=emle_species,
             alpha_mode=alpha_mode,
             atomic_numbers=(atomic_numbers if atomic_numbers is not None else None),
             mm_charges=mm_charges,