diff --git a/chgnet/graph/converter.py b/chgnet/graph/converter.py
index 4b26db59..3fb98f15 100644
--- a/chgnet/graph/converter.py
+++ b/chgnet/graph/converter.py
@@ -23,7 +23,7 @@
 except (ImportError, AttributeError):
     make_graph = None
 
-DATATYPE = torch.float32
+TORCH_DTYPE = torch.float32
 
 
 class CrystalGraphConverter(nn.Module):
@@ -124,10 +124,10 @@ def forward(
             requires_grad=False,
         )
         atom_frac_coord = torch.tensor(
-            structure.frac_coords, dtype=DATATYPE, requires_grad=True
+            structure.frac_coords, dtype=TORCH_DTYPE, requires_grad=True
         )
         lattice = torch.tensor(
-            structure.lattice.matrix, dtype=DATATYPE, requires_grad=True
+            structure.lattice.matrix, dtype=TORCH_DTYPE, requires_grad=True
         )
         center_index, neighbor_index, image, distance = structure.get_neighbor_list(
             r=self.atom_graph_cutoff, sites=structure.sites, numerical_tol=1e-8
@@ -177,7 +177,7 @@ def forward(
             atomic_number=atomic_number,
             atom_frac_coord=atom_frac_coord,
             atom_graph=atom_graph,
-            neighbor_image=torch.tensor(image, dtype=DATATYPE),
+            neighbor_image=torch.tensor(image, dtype=TORCH_DTYPE),
             directed2undirected=directed2undirected,
             undirected2directed=undirected2directed,
             bond_graph=bond_graph,
diff --git a/chgnet/graph/crystalgraph.py b/chgnet/graph/crystalgraph.py
index 637b359a..4f4572d1 100644
--- a/chgnet/graph/crystalgraph.py
+++ b/chgnet/graph/crystalgraph.py
@@ -9,7 +9,7 @@
 if TYPE_CHECKING:
     from typing_extensions import Self
 
-datatype = torch.float32
+TORCH_DTYPE = torch.float32
 
 
 class CrystalGraph:
diff --git a/chgnet/model/model.py b/chgnet/model/model.py
index d2030337..abca5b21 100644
--- a/chgnet/model/model.py
+++ b/chgnet/model/model.py
@@ -11,7 +11,7 @@
 from torch import Tensor, nn
 
 from chgnet.graph import CrystalGraph, CrystalGraphConverter
-from chgnet.graph.crystalgraph import datatype
+from chgnet.graph.crystalgraph import TORCH_DTYPE
 from chgnet.model.composition_model import AtomRef
 from chgnet.model.encoders import AngleEncoder, AtomEmbedding, BondEncoder
 from chgnet.model.functions import MLP, GatedMLP, find_normalization
@@ -808,7 +808,7 @@ def from_graphs(
             if compute_stress:
                 strain = graph.lattice.new_zeros([3, 3], requires_grad=True)
                 lattice = graph.lattice @ (
-                    torch.eye(3, dtype=datatype).to(strain.device) + strain
+                    torch.eye(3, dtype=TORCH_DTYPE).to(strain.device) + strain
                 )
             else:
                 strain = None
@@ -878,7 +878,7 @@ def from_graphs(
             torch.cat(atom_owners, dim=0).type(torch.int32).to(atomic_numbers.device)
         )
         directed2undirected = torch.cat(directed2undirected, dim=0)
-        volumes = torch.tensor(volumes, dtype=datatype, device=atomic_numbers.device)
+        volumes = torch.tensor(volumes, dtype=TORCH_DTYPE, device=atomic_numbers.device)
 
         return cls(
             atomic_numbers=atomic_numbers,
diff --git a/examples/make_graphs.py b/examples/make_graphs.py
index 8aacc2a5..15a5fcfe 100644
--- a/examples/make_graphs.py
+++ b/examples/make_graphs.py
@@ -10,7 +10,6 @@
 from chgnet.data.dataset import StructureData, StructureJsonData
 from chgnet.graph import CrystalGraphConverter
 
-datatype = torch.float32
 random.seed(100)