Docs about unit (#389)

examples/energy_force.py

@@ -32,6 +32,8 @@ model = torchani.models.ANI1ccx()
 # preceding ``1`` in the shape is here to support batch processing like in
 # training. If you have ``N`` different structures to compute, then make it
 # ``N``.
+#
+# The coordinates are in Angstrom, and the energies you get are in Hartree
 coordinates = torch.tensor([[[0.03192167, 0.00638559, 0.01301679],
                              [-0.83140486, 0.39370209, -0.26395324],
                              [-0.66518241, -0.84461308, 0.20759389],

torchani/aev.py

@@ -389,10 +389,11 @@ class AEVComputer(torch.nn.Module):
                 and pbc is boolean vector of size 3 storing if pbc is enabled
                 for that direction.
 
+                The coordinates, and cell are in Angstrom.
+
         Returns:
             NamedTuple: Species and AEVs. species are the species from the input
-            unchanged, and AEVs is a tensor of shape
-            ``(C, A, self.aev_length())``
+            unchanged, and AEVs is a tensor of shape ``(C, A, self.aev_length())``
         """
         species, coordinates = input_
 

torchani/models.py

@@ -101,6 +101,9 @@ class BuiltinNet(torch.nn.Module):
 
         Returns:
             species_energies: energies for the given configurations
+
+        .. note:: The coordinates, and cell are in Angstrom, and the energies
+            will be in Hartree.
         """
         species_aevs = self.aev_computer(species_coordinates, cell=cell, pbc=pbc)
         species_energies = self.neural_networks(species_aevs)

torchani/nn.py

@@ -9,12 +9,14 @@ class SpeciesEnergies(NamedTuple):
 
 
 class ANIModel(torch.nn.Module):
-    """ANI model that compute properties from species and AEVs.
+    """ANI model that compute energies from species and AEVs.
 
     Different atom types might have different modules, when computing
-    properties, for each atom, the module for its corresponding atom type will
+    energies, for each atom, the module for its corresponding atom type will
     be applied to its AEV, after that, outputs of modules will be reduced along
-    different atoms to obtain molecular properties.
+    different atoms to obtain molecular energies.
+
+    The resulting energies are in Hartree.
 
     Arguments:
         modules (:class:`collections.abc.Sequence`): Modules for each atom